Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
A novel hybrid deep learning model for price prediction IJECEIAES
Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNNLSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
A Novel approach to Fake News Detection using Bi-directional LSTM Neural Netw...IRJET Journal
This document presents a novel approach to detecting fake news using a bidirectional long short-term memory (Bi-LSTM) neural network model with sequential attention. The proposed model employs a Bi-LSTM architecture to analyze news articles in both forward and backward directions. It also incorporates sequential attention to help the model discern patterns in textual data and boost accuracy in detecting false news. The model is evaluated on publicly available news datasets and shows superior performance compared to alternative methods such as convolutional neural networks and recurrent neural networks.
Pca based support vector machine technique for volatility forecastingeSAT Publishing House
This document discusses using principal component analysis (PCA) as a feature extraction technique combined with support vector machines (SVM) for volatility forecasting of stock market data. PCA is used to extract features from the volatility time series that describe the varying risk estimates. These features are then fed into an SVM model with a Gaussian kernel to perform nonlinear classification and forecasting. The proposed PCA+SVM technique is evaluated on real stock market benchmark datasets and shown to perform better than SVM alone for volatility forecasting.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
Predicting Stock Market Prices with Sentiment Analysis and Ensemble Learning ...IRJET Journal
The document describes a study that proposes a hybrid approach for predicting stock market prices using sentiment analysis and ensemble learning techniques. The approach involves collecting stock price and social media data, performing sentiment analysis on the text data, combining the datasets, training various machine learning models, and evaluating the models based on metrics like RMSE and R2 score. The results found that the ensemble XGBoost model outperformed individual models like LSTM and linear regression in predicting stock prices of companies, demonstrating the potential of using sentiment analysis and ensemble learning for stock market prediction.
A novel hybrid deep learning model for price prediction IJECEIAES
Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNNLSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
A Novel approach to Fake News Detection using Bi-directional LSTM Neural Netw...IRJET Journal
This document presents a novel approach to detecting fake news using a bidirectional long short-term memory (Bi-LSTM) neural network model with sequential attention. The proposed model employs a Bi-LSTM architecture to analyze news articles in both forward and backward directions. It also incorporates sequential attention to help the model discern patterns in textual data and boost accuracy in detecting false news. The model is evaluated on publicly available news datasets and shows superior performance compared to alternative methods such as convolutional neural networks and recurrent neural networks.
Pca based support vector machine technique for volatility forecastingeSAT Publishing House
This document discusses using principal component analysis (PCA) as a feature extraction technique combined with support vector machines (SVM) for volatility forecasting of stock market data. PCA is used to extract features from the volatility time series that describe the varying risk estimates. These features are then fed into an SVM model with a Gaussian kernel to perform nonlinear classification and forecasting. The proposed PCA+SVM technique is evaluated on real stock market benchmark datasets and shown to perform better than SVM alone for volatility forecasting.
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...ijaia
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
A NOVEL SCHEME FOR ACCURATE REMAINING USEFUL LIFE PREDICTION FOR INDUSTRIAL I...gerogepatton
In the era of the fourth industrial revolution, measuring and ensuring the reliability, efficiency and safety of the industrial systems and components are one of the uppermost key concern. In addition, predicting performance degradation or remaining useful life (RUL) of an equipment over time based on its historical sensor data enables companies to greatly reduce their maintenance cost. In this way, companies can prevent costly unexpected breakdown and become more profitable and competitive in the marketplace. This paper introduces a deep learning-based method by combining CNN(Convolutional Neural Networks) and LSTM (Long Short-Term Memory)neural networks to predict RUL for industrial equipment. The proposed method does not depend upon any degradation trend assumptions and it can learn complex temporal representative and distinguishing patterns in the sensor data. In order to evaluate the efficiency and effectiveness of the proposed method, we evaluated it on two different experiment: RUL estimation and predicting the status of the IoT devices in 2-week period. Experiments are conducted on a publicly available NASA’s turbo fan-engine dataset. Based on the experiment results, the deep learning-based approach achieved high prediction accuracy. Moreover, the results show that the method outperforms standard well-accepted machine learning algorithms and accomplishes competitive performance when compared to the state-of-the art methods
Predicting Stock Market Prices with Sentiment Analysis and Ensemble Learning ...IRJET Journal
The document describes a study that proposes a hybrid approach for predicting stock market prices using sentiment analysis and ensemble learning techniques. The approach involves collecting stock price and social media data, performing sentiment analysis on the text data, combining the datasets, training various machine learning models, and evaluating the models based on metrics like RMSE and R2 score. The results found that the ensemble XGBoost model outperformed individual models like LSTM and linear regression in predicting stock prices of companies, demonstrating the potential of using sentiment analysis and ensemble learning for stock market prediction.
A Compendium of Various Applications of Machine LearningIRJET Journal
This document provides a review of various applications of machine learning. It begins with an introduction to machine learning and discusses its applications in fields such as energy efficiency, intrusion detection, anomaly detection, quantitative finance, and cancer prediction and prognosis. Specific machine learning algorithms and techniques discussed include decision trees, naive Bayes, k-nearest neighbors, artificial neural networks, support vector machines, and more. The document also provides examples of machine learning applications in each field and references various research papers to support the discussed applications.
The Utilization of Machine Learning for The Prediction of CSX Index and Tax R...AJHSSR Journal
ABSTRACT: The machine learning model LSTM was applied and compared with the linear regression machine
learning model for the CSX index of the Cambodia Securities Exchange. The time span covered in this study was
from February 1, 2019, to October 12, 2023, totaling 1146 days. Out of these, 917 days were classified as Train
data, accounting for 80% of the total sample size, while 229 days were classified as Test data. Additionally, the
same techniques were used to forecast the tax revenues of the Royal Government of Cambodia, which were
collected by the General Department of Taxation. Despite using monthly data, the tax revenues were analyzed
over a longer period of 25 years, starting from January 1998 to August 2023. The Train data consisted of 247
months, equivalent to 80% of the total sample size, while the Test data accounted for 61 months, approximately
20% of the total sample size. The machine learning model, LSTM, demonstrated superior performance over the
linear regression machine learning model in predicting the daily movement of the CSX Index of the Cambodia
Securities Exchange, as evidenced by the root mean square error. The Test data revealed that the estimated root
mean square error of the linear regression machine learning was 64.78, while the LSTM machine learning
produced a lower error of 33.08. However, when applied to tax revenues data, the linear regression machine
learning outperformed the LSTM machine learning, with estimated root mean square errors of 219.31 and
1601.87, respectively.
KEYWORDS: CSX Index, Tax Revenues, Machine Learning, Linear Regression, LSTM
Time Series Weather Forecasting Techniques: Literature SurveyIRJET Journal
This document summarizes various time series forecasting techniques discussed in literature, including ARIMA, Prophet, and LSTM models. It reviews their applications in weather forecasting, analyzing COVID-19 data, real estate prices, bitcoin values, and more. The key techniques are compared based on their forecasting accuracy on different datasets. ARIMA is generally good at capturing trends but requires stationary data, while Prophet and LSTM can handle non-stationary data and seasonal effects better. Prophet achieved 91% accuracy on a COVID dataset, outperforming ARIMA. LSTM achieved 76% accuracy for rainfall forecasting. The document concludes different approaches are still being developed to address the unique challenges of weather data forecasting.
This document provides details about a project aimed at predicting stock market values using Hidden Markov Models. It includes an introduction describing the problem of stock market prediction and the suitability of HMMs for tackling the time-dependent nature of the problem. The document outlines the approach taken, which involves using the daily fractional change in stock value and fractional deviation of intra-day high and low values to train separate HMMs for different stocks. It then discusses testing the models on various stocks and comparing performance to other existing methods. Tables and figures are provided to illustrate the experimental setup, results, and risk analysis.
With the development of the urbanization, industrialization and populace, there has been a huge development in the rush hour gridlock. With development in the rush hour gridlock, there got a heap of issues with it as well, these issues incorporate congested roads, mishaps and movement govern infringement at the overwhelming activity signals. This thusly adversy affects the economy of the nation and in addition the loss of lives. Thus, Speed control is in the need of great importance because of the expanded rate of mishaps announced in our everyday life. The criminal traffic offense expanded due to over movement on streets. The reason is rapid of vehicles. The speed of the vehicles is past the normal speed confine is called speed infringement. In this paper diverse issues are confronted that are given in issue detailing. Every one of these issues are in future with the assistance of the fortification learning issue and advancement issue the changed neural system is contemplated with NN calculations forward Chaining back spread . Omesh Goyal | Chamkour Singh ""A Review on Traffic Signal Identification"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23557.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23557/a-review-on-traffic-signal-identification/omesh-goyal
MUTUAL FUND RECOMMENDATION SYSTEM WITH PERSONALIZED EXPLANATIONSIRJET Journal
This document summarizes a research paper that proposes a mutual fund recommendation system that provides personalized explanations for its recommendations. It begins with an introduction that describes the need for such a system given the challenges that investors face in choosing appropriate mutual funds. It then reviews existing recommendation models and discusses challenges such as lack of data and cold start problems. The literature survey summarizes several papers on topics like knowledge graph-based recommendation systems, personalized equity recommendation using transfer learning, using time-series models like Prophet for predicting fund prices, network analysis approaches for portfolio recommendations, and using machine learning like deep learning for stock market and mutual fund predictions. The overall goal is to develop a model that can provide personalized mutual fund recommendations along with explanations.
This paper aims to build predictive models using the CRISP-DM framework to classify bank customers into predefined classes using a Portuguese marketing campaign dataset. It analyzes bank customer data containing over 45,000 instances and 16 features to classify customers as likely or not likely to subscribe to bank deposits. It uses multilayer perceptron and logistic regression algorithms for modeling. The results show that the multilayer perceptron model with a 70% training split provides the best average performance, accurately classifying customers 70% of the time.
Simulation in the supply chain context a survey Sergio Terzia,.docxbudabrooks46239
Simulation in the supply chain context: a survey
Sergio Terzia,*, Sergio Cavalierib a Politecnico di Milano, Department of Economics, Industrial and Management Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy b Department of Industrial Engineering, Universita` di Bergamo, Viale Marconi 5, 24044 Dalmine, Italy Received 29 January 2003; accepted 13 June 2003
Abstract
The increased level of competitiveness in all industrial sectors, exacerbated in the last years by the globalisation of the economies and by the sharp fall of the final demands, are pushing enterprises to strive for a further optimisation of their organisational processes, and in particular to pursue new forms of collaboration and partnership with their direct logistics counterparts. As a result, at a company level there is a progressive shift towards an external perspective with the design and implementation of new management strategies, which are generally named with the term of supply chain management (SCM). However, despite the flourish of several IT solutions in this context, there are still evident hurdles to overcome, mainly due to the major complexity of the problems to be tackled in a logistics network and to the conflicts resulting from local objectives versus network strategies. Among the techniques supporting a multi-decisional context, as a supply chain (SC) is, simulation can undoubtedly play an important role, above all for its main property to provide what-if analysis and to evaluate quantitatively benefits and issues deriving from operating in a co-operative environment rather than playing a pure transaction role with the upstream/downstream tiers. The paper provides a comprehensive review made on more than 80 articles, with the main purpose of ascertaining which general objectives simulation is generally called to solve, which paradigms and simulation tools are more suitable, and deriving useful prescriptions both for practitioners and researchers on its applicability in decision-making processes within the supply chain context. # 2003 Elsevier B.V. All rights reserved. Keywords: Parallel and distributed simulation; Supply chain management; High level architecture; Survey 1. Introduction Modern industrial enterprises operate in a rapidly changing world, stressed by even more global competition, managing world-wide procurement and unforeseeable markets, supervising geographically distributed production plants, striving for the provision of outstanding products and high quality customer service. More than in the past, companies which are not able to revise periodically their strategies and, accordingly, to modify their organisational processes seriously risk to be pulled out from the competitive edge. In the 1990s, companies have made huge efforts for streamlining their internal business processes, identifying and enhancing the core activities pertaining to the product value chain, and invested massively in new intra-company information and communicat.
Stock Market Prediction using Long Short-Term MemoryIRJET Journal
This document discusses using a Long Short-Term Memory (LSTM) model to predict stock market prices. It begins by introducing the problem of predicting stock markets and how machine learning techniques like LSTM can help. It then discusses collecting stock price data and designing an LSTM model in Python using Keras and other libraries. The model is trained on historical stock price data to identify patterns and predict future prices. The document suggests LSTM models are well-suited for this due to their ability to use past data in predictions. It evaluates the model's predictions against actual prices to determine accuracy.
This document presents research on predicting stock market trends in Tehran, Iran using machine learning and deep learning algorithms. Ten years of historical data from four stock market groups were analyzed using nine machine learning models (Decision Tree, Random Forest, Adaboost, XGBoost, SVC, Naive Bayes, KNN, Logistic Regression, ANN) and two deep learning models (RNN, LSTM). Ten technical indicators were used as input values in both continuous and binary formats to evaluate the models. The results showed that RNN and LSTM performed best on continuous data, outperforming other models, while on binary data they still performed best but with less difference between models due to improved performance.
IRJET- Weather Prediction for Tourism Application using ARIMAIRJET Journal
This document discusses using an ARIMA model to predict weather patterns for tourism applications. It begins with an introduction to weather forecasting and its importance for the tourism industry. It then reviews related work on weather prediction using machine learning methods. The proposed method involves collecting weather data, preprocessing it, converting it to a stationary time series, analyzing it using an ARIMA model, and concluding that ARIMA can accurately predict weather patterns to help tourists plan trips based on the forecast.
Towards a new intelligent traffic system based on deep learning and data int...IJECEIAES
Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
Stock Market Prediction Using Deep LearningIRJET Journal
This document summarizes research on using deep learning techniques to predict stock market prices. Specifically, it discusses prior research that has used models like LSTM, CNN, random forest and logistic regression with technical indicators as inputs to predict stock prices, trends and trading signals. It also outlines some of the challenges in making accurate stock predictions, such as accessing reliable market data and accounting for the large volume of time series data. The literature review covers several papers that have developed and evaluated deep learning models for stock prediction and generated trading signals.
This document summarizes a student project on stock market price prediction using machine learning. It includes an introduction discussing the importance of stock price prediction and the potential of machine learning techniques. It then covers system analysis aspects for developing predictive models, including problem definition, data collection/preprocessing, feature engineering, model selection/evaluation, and ensuring model interpretability. The overall aim is to explore applying machine learning algorithms to effectively forecast stock prices.
A MODEL-BASED APPROACH MACHINE LEARNING TO SCALABLE PORTFOLIO SELECTIONIJCI JOURNAL
This study proposes a scalable asset selection and allocation approach using machine learning that
integrates clustering methods into portfolio optimization models. The methodology applies the Uniform
Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets
from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the
Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the
pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading
days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of
20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated
through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio
allocation. Overall, this study highlights the potential of machine learning in portfolio optimization,
providing a useful framework for investment practitioners.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then presents a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze costs for different companies. The conclusion states that mathematical programming allows comparison of costs between products and optimization of production costs and systems.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then describes a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document also discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze different companies' supply chain costs.
STOCK MARKET PREDICTION AND ANALYSIS USING MACHINE LEARNING ALGORITHMSIRJET Journal
This document discusses using machine learning algorithms like LSTM and linear regression to predict stock market prices. It proposes using techniques like stacked LSTM on historical stock data to make predictions. The document outlines collecting data, preprocessing it, training models on training data and evaluating them on test data. It suggests comparing the accuracy of linear regression, stacked LSTM and other models to determine the most accurate for predicting 30 days of future stock prices. The goal is to reduce investment risk through more accurate machine-learned stock predictions.
Supplier Selection and Evaluation by Fuzzy-AHP Extent Analysis: A Case Study ...Dr. Amarjeet Singh
The ready-made garments (RMG)is a rapid
growing industry in Bangladesh and contributing
significantly in the country’s economy. Effective supplier
selection policy has significant strategic importance in the
performance of such fast moving consumer goods industry.
The supplier selection process is essentially a multi-criterion
decision making problem which, therefore, must be
developed systematically. Many models have been developed
and proposed to find optimum solutions of this complex
decision-making problem. Fuzzy Analytic Hierarchy
Process (Fuzzy-AHP), which is a derived extension of
classical Analytical Hierarchy Process (AHP),is an excellent
method for deciding among the complex structure at
different levels. In this paper an extent analysis of FuzzyAHP has been applied to evaluate and select the best
supplier agency providing most satisfaction. The evaluation
criteria are developed particularly for an RMG
manufacturer in Bangladesh context and used successfully
in the proposed model. A detailed implementation process is
presented in this paper and finally the best supplier agency
has been proposed from the outcome of the model.
Quantitative and Qualitative Analysis of Time-Series Classification using Dee...Nader Ale Ebrahim
Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords co-occurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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Time Series Weather Forecasting Techniques: Literature SurveyIRJET Journal
This document summarizes various time series forecasting techniques discussed in literature, including ARIMA, Prophet, and LSTM models. It reviews their applications in weather forecasting, analyzing COVID-19 data, real estate prices, bitcoin values, and more. The key techniques are compared based on their forecasting accuracy on different datasets. ARIMA is generally good at capturing trends but requires stationary data, while Prophet and LSTM can handle non-stationary data and seasonal effects better. Prophet achieved 91% accuracy on a COVID dataset, outperforming ARIMA. LSTM achieved 76% accuracy for rainfall forecasting. The document concludes different approaches are still being developed to address the unique challenges of weather data forecasting.
This document provides details about a project aimed at predicting stock market values using Hidden Markov Models. It includes an introduction describing the problem of stock market prediction and the suitability of HMMs for tackling the time-dependent nature of the problem. The document outlines the approach taken, which involves using the daily fractional change in stock value and fractional deviation of intra-day high and low values to train separate HMMs for different stocks. It then discusses testing the models on various stocks and comparing performance to other existing methods. Tables and figures are provided to illustrate the experimental setup, results, and risk analysis.
With the development of the urbanization, industrialization and populace, there has been a huge development in the rush hour gridlock. With development in the rush hour gridlock, there got a heap of issues with it as well, these issues incorporate congested roads, mishaps and movement govern infringement at the overwhelming activity signals. This thusly adversy affects the economy of the nation and in addition the loss of lives. Thus, Speed control is in the need of great importance because of the expanded rate of mishaps announced in our everyday life. The criminal traffic offense expanded due to over movement on streets. The reason is rapid of vehicles. The speed of the vehicles is past the normal speed confine is called speed infringement. In this paper diverse issues are confronted that are given in issue detailing. Every one of these issues are in future with the assistance of the fortification learning issue and advancement issue the changed neural system is contemplated with NN calculations forward Chaining back spread . Omesh Goyal | Chamkour Singh ""A Review on Traffic Signal Identification"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23557.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23557/a-review-on-traffic-signal-identification/omesh-goyal
MUTUAL FUND RECOMMENDATION SYSTEM WITH PERSONALIZED EXPLANATIONSIRJET Journal
This document summarizes a research paper that proposes a mutual fund recommendation system that provides personalized explanations for its recommendations. It begins with an introduction that describes the need for such a system given the challenges that investors face in choosing appropriate mutual funds. It then reviews existing recommendation models and discusses challenges such as lack of data and cold start problems. The literature survey summarizes several papers on topics like knowledge graph-based recommendation systems, personalized equity recommendation using transfer learning, using time-series models like Prophet for predicting fund prices, network analysis approaches for portfolio recommendations, and using machine learning like deep learning for stock market and mutual fund predictions. The overall goal is to develop a model that can provide personalized mutual fund recommendations along with explanations.
This paper aims to build predictive models using the CRISP-DM framework to classify bank customers into predefined classes using a Portuguese marketing campaign dataset. It analyzes bank customer data containing over 45,000 instances and 16 features to classify customers as likely or not likely to subscribe to bank deposits. It uses multilayer perceptron and logistic regression algorithms for modeling. The results show that the multilayer perceptron model with a 70% training split provides the best average performance, accurately classifying customers 70% of the time.
Simulation in the supply chain context a survey Sergio Terzia,.docxbudabrooks46239
Simulation in the supply chain context: a survey
Sergio Terzia,*, Sergio Cavalierib a Politecnico di Milano, Department of Economics, Industrial and Management Engineering, Piazza Leonardo da Vinci 32, 20133 Milan, Italy b Department of Industrial Engineering, Universita` di Bergamo, Viale Marconi 5, 24044 Dalmine, Italy Received 29 January 2003; accepted 13 June 2003
Abstract
The increased level of competitiveness in all industrial sectors, exacerbated in the last years by the globalisation of the economies and by the sharp fall of the final demands, are pushing enterprises to strive for a further optimisation of their organisational processes, and in particular to pursue new forms of collaboration and partnership with their direct logistics counterparts. As a result, at a company level there is a progressive shift towards an external perspective with the design and implementation of new management strategies, which are generally named with the term of supply chain management (SCM). However, despite the flourish of several IT solutions in this context, there are still evident hurdles to overcome, mainly due to the major complexity of the problems to be tackled in a logistics network and to the conflicts resulting from local objectives versus network strategies. Among the techniques supporting a multi-decisional context, as a supply chain (SC) is, simulation can undoubtedly play an important role, above all for its main property to provide what-if analysis and to evaluate quantitatively benefits and issues deriving from operating in a co-operative environment rather than playing a pure transaction role with the upstream/downstream tiers. The paper provides a comprehensive review made on more than 80 articles, with the main purpose of ascertaining which general objectives simulation is generally called to solve, which paradigms and simulation tools are more suitable, and deriving useful prescriptions both for practitioners and researchers on its applicability in decision-making processes within the supply chain context. # 2003 Elsevier B.V. All rights reserved. Keywords: Parallel and distributed simulation; Supply chain management; High level architecture; Survey 1. Introduction Modern industrial enterprises operate in a rapidly changing world, stressed by even more global competition, managing world-wide procurement and unforeseeable markets, supervising geographically distributed production plants, striving for the provision of outstanding products and high quality customer service. More than in the past, companies which are not able to revise periodically their strategies and, accordingly, to modify their organisational processes seriously risk to be pulled out from the competitive edge. In the 1990s, companies have made huge efforts for streamlining their internal business processes, identifying and enhancing the core activities pertaining to the product value chain, and invested massively in new intra-company information and communicat.
Stock Market Prediction using Long Short-Term MemoryIRJET Journal
This document discusses using a Long Short-Term Memory (LSTM) model to predict stock market prices. It begins by introducing the problem of predicting stock markets and how machine learning techniques like LSTM can help. It then discusses collecting stock price data and designing an LSTM model in Python using Keras and other libraries. The model is trained on historical stock price data to identify patterns and predict future prices. The document suggests LSTM models are well-suited for this due to their ability to use past data in predictions. It evaluates the model's predictions against actual prices to determine accuracy.
This document presents research on predicting stock market trends in Tehran, Iran using machine learning and deep learning algorithms. Ten years of historical data from four stock market groups were analyzed using nine machine learning models (Decision Tree, Random Forest, Adaboost, XGBoost, SVC, Naive Bayes, KNN, Logistic Regression, ANN) and two deep learning models (RNN, LSTM). Ten technical indicators were used as input values in both continuous and binary formats to evaluate the models. The results showed that RNN and LSTM performed best on continuous data, outperforming other models, while on binary data they still performed best but with less difference between models due to improved performance.
IRJET- Weather Prediction for Tourism Application using ARIMAIRJET Journal
This document discusses using an ARIMA model to predict weather patterns for tourism applications. It begins with an introduction to weather forecasting and its importance for the tourism industry. It then reviews related work on weather prediction using machine learning methods. The proposed method involves collecting weather data, preprocessing it, converting it to a stationary time series, analyzing it using an ARIMA model, and concluding that ARIMA can accurately predict weather patterns to help tourists plan trips based on the forecast.
Towards a new intelligent traffic system based on deep learning and data int...IJECEIAES
Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
Stock Market Prediction Using Deep LearningIRJET Journal
This document summarizes research on using deep learning techniques to predict stock market prices. Specifically, it discusses prior research that has used models like LSTM, CNN, random forest and logistic regression with technical indicators as inputs to predict stock prices, trends and trading signals. It also outlines some of the challenges in making accurate stock predictions, such as accessing reliable market data and accounting for the large volume of time series data. The literature review covers several papers that have developed and evaluated deep learning models for stock prediction and generated trading signals.
This document summarizes a student project on stock market price prediction using machine learning. It includes an introduction discussing the importance of stock price prediction and the potential of machine learning techniques. It then covers system analysis aspects for developing predictive models, including problem definition, data collection/preprocessing, feature engineering, model selection/evaluation, and ensuring model interpretability. The overall aim is to explore applying machine learning algorithms to effectively forecast stock prices.
A MODEL-BASED APPROACH MACHINE LEARNING TO SCALABLE PORTFOLIO SELECTIONIJCI JOURNAL
This study proposes a scalable asset selection and allocation approach using machine learning that
integrates clustering methods into portfolio optimization models. The methodology applies the Uniform
Manifold Approximation and Projection method and ensemble clustering techniques to preselect assets
from the Ibovespa and S&P 500 indices. The research compares three allocation models and finds that the
Hierarchical Risk Parity model outperformed the others, with a Sharpe ratio of 1.11. Despite the
pandemic's impact on the portfolios, with drawdowns close to 30%, they recovered in 111 to 149 trading
days. The portfolios outperformed the indices in cumulative returns, with similar annual volatilities of
20%. Preprocessing with UMAP allowed for finding clusters with higher discriminatory power, evaluated
through internal cluster validation metrics, helping to reduce the problem's size during optimal portfolio
allocation. Overall, this study highlights the potential of machine learning in portfolio optimization,
providing a useful framework for investment practitioners.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then presents a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze costs for different companies. The conclusion states that mathematical programming allows comparison of costs between products and optimization of production costs and systems.
This document discusses the use of mathematical programming to optimize supply chain management. It begins with an introduction to mathematical programming and its applications in supply chain management. It then describes a generic mixed-integer programming model for supply chain configuration that aims to minimize total costs. The model includes constraints related to demand fulfillment, facility flows, capacity, material availability and open facilities. The document also discusses common modifications to the generic model, such as incorporating international factors, inventory, transportation and policies. It provides two case studies that apply the generic model to analyze different companies' supply chain costs.
STOCK MARKET PREDICTION AND ANALYSIS USING MACHINE LEARNING ALGORITHMSIRJET Journal
This document discusses using machine learning algorithms like LSTM and linear regression to predict stock market prices. It proposes using techniques like stacked LSTM on historical stock data to make predictions. The document outlines collecting data, preprocessing it, training models on training data and evaluating them on test data. It suggests comparing the accuracy of linear regression, stacked LSTM and other models to determine the most accurate for predicting 30 days of future stock prices. The goal is to reduce investment risk through more accurate machine-learned stock predictions.
Supplier Selection and Evaluation by Fuzzy-AHP Extent Analysis: A Case Study ...Dr. Amarjeet Singh
The ready-made garments (RMG)is a rapid
growing industry in Bangladesh and contributing
significantly in the country’s economy. Effective supplier
selection policy has significant strategic importance in the
performance of such fast moving consumer goods industry.
The supplier selection process is essentially a multi-criterion
decision making problem which, therefore, must be
developed systematically. Many models have been developed
and proposed to find optimum solutions of this complex
decision-making problem. Fuzzy Analytic Hierarchy
Process (Fuzzy-AHP), which is a derived extension of
classical Analytical Hierarchy Process (AHP),is an excellent
method for deciding among the complex structure at
different levels. In this paper an extent analysis of FuzzyAHP has been applied to evaluate and select the best
supplier agency providing most satisfaction. The evaluation
criteria are developed particularly for an RMG
manufacturer in Bangladesh context and used successfully
in the proposed model. A detailed implementation process is
presented in this paper and finally the best supplier agency
has been proposed from the outcome of the model.
Quantitative and Qualitative Analysis of Time-Series Classification using Dee...Nader Ale Ebrahim
Time-series classification is utilized in a variety of applications leading to the development of many data mining techniques for time-series analysis. Among the broad range of time-series classification algorithms, recent studies are considering the impact of deep learning methods on time-series classification tasks. The quantity of related publications requires a bibliometric study to explore most prominent keywords, countries, sources and research clusters. The paper conducts a bibliometric analysis on related publications in time-series classification, adopted from Scopus database between 2010 and 2019. Through keywords co-occurrence analysis, a visual network structure of top keywords in time-series classification research has been produced and deep learning has been introduced as the most common topic by additional inquiry of the bibliography. The paper continues by exploring the publication trends of recent deep learning approaches for time-series classification. The annual number of publications, the productive and collaborative countries, the growth rate of sources, the most occurred keywords and the research collaborations are revealed from the bibliometric analysis within the study period. The research field has been broken down into three main categories as different frameworks of deep neural networks, different applications in remote sensing and also in signal processing for time-series classification tasks. The qualitative analysis highlights the categories of top citation rate papers by describing them in details.
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Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
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Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
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Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
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1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
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20240702 QFM021 Machine Intelligence Reading List June 2024
Efficient commodity price forecasting using long short-term memory model
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 14, No. 1, March 2024, pp. 994~1004
ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i1.pp994-1004 994
Journal homepage: http://ijai.iaescore.com
Efficient commodity price forecasting using long short-term
memory model
Mohammad Tami, Amani Yousef Owda
Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah, Palestine
Article Info ABSTRACT
Article history:
Received Aug 2, 2023
Revised Oct 16, 2023
Accepted Oct 29, 2023
Predicting commodity prices, particularly food prices, is a significant
concern for various stakeholders, especially in regions that are highly
sensitive to commodity price volatility. Historically, many machine learning
models like autoregressive integrated moving average (ARIMA) and support
vector machine (SVM) have been suggested to overcome the forecasting
task. These models struggle to capture the multifaceted and dynamic factors
influencing these prices. Recently, deep learning approaches have
demonstrated considerable promise in handling complex forecasting tasks.
This paper presents a novel long short-term memory (LSTM) network-based
model for commodity price forecasting. The model uses five essential
commodities namely bread, meat, milk, oil, and petrol. The proposed model
focuses on advanced feature engineering which involves moving averages,
price volatility, and past prices. The results reveal that our model
outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for
root mean square error (RMSE), mean absolute percentage error (MAPE),
and R-squared (R2
), respectively. In addition to the simplicity of the model,
which consists of an LSTM single-cell architecture that reduced the training
time to a few minutes instead of hours. This paper contributes to the
economic literature on price prediction using advanced deep learning
techniques as well as provides practical implications for managing
commodity price instability globally.
Keywords:
Deep learning
Feature engineering
Long short-term memory
Price instability
Time-series forecasting
This is an open access article under the CC BY-SA license.
Corresponding Author:
Amani Yousef Owda
Department of Natural, Engineering and Technology Sciences, Arab American University
Ramallah P600, Palestine
Email: amani.Owda@aaup.edu
1. INTRODUCTION
Commodity price forecasting is an essential task for stakeholders such as governments,
policymakers, retailers, and customers. Accurate commodity price forecasting has a huge impact on
managing inflation, securing commodity supplies, and avoiding socio-economy disruptions [1], [2].
Particularly in a world marked by interconnected economies and complex trade relationships [3], precise
commodity price forecasts can aid in avoiding crises and promoting stability in the global commodity supply
chain [4]. In recent years, commodity price fluctuation has been a significant concern for stakeholders around
the world. Several factors have contributed to these fluctuations, leading to challenges in managing
commodity supplies, inflation, and socio-economic stability [5]-[8]. Particularly, food price fluctuations can
have far-reaching consequences, especially for vulnerable populations in low-income countries, where a
significant portion of income is spent on food. High and unpredictable food prices can lead to food
insecurity, malnutrition, and social unrest [9], [10]. Governments, policymakers, and international
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organizations continue to work on measures to mitigate the impact of these fluctuations. Accurate food price
forecasting remains a crucial tool in managing these challenges and promoting global food security [11]-[13].
Many traditional statistics and machine learning (ML) models have been utilized to accomplish
commodity price prediction [14]-[18], specifically food price forecasting [19]-[22]. These models are easy to
implement and don’t require huge computing power. However, these models failed to capture complex
patterns and trends in the time-series data since they have prior assumptions about the data [23], [24]. With
the rise of deep learning (DL) power, neural network (NN) models gain attraction for being used in many
downstream forecasting tasks [25], [26]. Historically, different methods have been applied in commodity
price prediction. These include statistical approaches like autoregressive integrated moving average
(ARIMA) [27] and seasonal autoregressive integrated moving average (SARIMA) [28], machine learning
techniques such as support vector machine (SVM) [29], and deep learning models like long short-term
memory (LSTM) [30], [31]. LSTM models are often preferred over other models for time series forecasting
due to their ability to capture long-term dependencies and handle sequential data effectively [32]. LSTM's
non-parametric characteristic, combined with its ability to handle non-linear patterns and its independence
from the need for a stationary process, make it a favorable option for implementation in time-series
applications [33].
Various researchers have extended LSTM models to improve forecasting performance. For instance,
Ly et al. [34] introduced a hybrid model that combines the strengths of LSTM and ARIMA to forecast the
prices of cotton and oil, their approach depends on training two separated models, namely LSTM and
ARIMA, then averaging the forecasting result from both trained models to achieve better result compared to
each individual model. In contrast, Krishnan et al. [35] utilized a diverse set of complex LSTM models,
namely basic LSTM, bidirectional LSTM (Bi-LSTM), stacked LSTM, convolutional neural network LSTM
(CNN-LSTM), and convolutional LSTM (Conv-LSTM), in their five-commodity forecasting study. Each of
these models offered unique architectural variations to explore and analyze their predictive capabilities. The
authors argued that their complex models are capable of capturing complex patterns and dependencies within
the commodity market data, allowing for more accurate and robust predictions. On the other hand, the use of
deep learning models in combination with natural language processing such as transformer-based models
[36] and bidirectional encoder representations from transformers (BERT) [37] has been gaining traction in
recent years due to their ability for capturing market sentiment tracking and context-aware analysis. For
instance, Sonkiya et al. [38] proposed a generative adversarial network (GAN) combined with BERT model
for predicting the price of stock predictions, in their study, BERT is utilized to analyze news and headlines,
extracting valuable insights. These insights are then incorporated into the GAN model as external factors,
which lead to enhancing their model's predictive capabilities.
Despite the advancements in machine learning and deep learning models, there remains a need for
models that are not only accurate but also interpretable and computationally efficient. The black-box nature
of many deep learning models often leads to a need for more interpretability, which can slow down their
adoption in certain sectors [39]. On the other hand, the computational requirements of these models can be a
limiting factor, especially in low-resource settings [40]. This calls for the development of models that balance
accuracy, interpretability, and efficiency, which is the focus of this paper. This paper aims to address these
limitations by introducing a straightforward effective LSTM model for predicting the prices of various
commodities. The developed model demonstrates comparable performance compared to other deep learning
models. The paper approach places significant emphasis on the feature engineering aspect of the task. It was
found that implementing diverse feature transformations, such as moving averages and volatility changes,
greatly enhances the performance of the suggested model. The model's simplicity and efficacy, combined
with less demand for computational resources, suggests its potential for broader application in commodity
price forecasting. Future work could involve the inclusion of external factors such as social media sentiment
and news headlines into the model to further improve its predictive performance.
2. METHODOLOGY
A comprehensive methodology is used in this paper to address the research objectives effectively.
Figure 1 illustrates the chronological flow chart of the adapted methodology. The first step involved data
acquisition. Subsequently, a data cleaning process was performed where some missing prices were imputed
by the average of the lag and lead prices. Next, an exploratory data analysis (EDA) phase was conducted to
gain deeper insights into the dataset, identify patterns, and establish descriptive statistics that aid in
understanding the underlying characteristics of the data. Following the EDA, feature engineering techniques
were employed to transform the raw data into meaningful and informative features that can enhance the
performance of the models. After feature engineering, data transformation techniques were applied to
normalize the data, ensuring that all variables are on a comparable scale. This step helps in improving the
model's convergence and performance. Subsequently, the modeling phase involved a training loop, wherein
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various hyperparameters were tested to optimize the model's performance and achieve the best possible
results. Finally, the performance of the trained models was evaluated using appropriate evaluation metrics to
assess their effectiveness in solving the research problem. This methodology ensures a systematic and
rigorous approach to conducting the study, enabling reliable and insightful conclusions to be drawn from the
analysis. Through this research, we will illustrate the process of choosing appropriate features, transforming
them suitably, and training an LSTM model for forecasting commodity prices. The outcome is expected to
provide valuable insights for stakeholders in the commodity industry, enhancing their decision-making
capabilities and promoting more efficient practices.
2.1. Dataset
The foundation of the proposed model relies on an extensive dataset that encloses a detailed history
of various commodity prices within the State of Palestine. The origin of this dataset is the World Food
Program Price Database [41], an extensive global source of commodity price information that spans 98
countries and approximately 3,000 distinct markets. The database audits prices for many commodities,
including but not limited to bread, meat, milk, oil, and petrol. Although the database is refreshed weekly, the
availability of monthly data is more frequent. Specifically, the dataset under consideration for current
research referred to as 'Palestine Food Price Dataset', boasts a rich history of commodity prices in Palestine
dating back to 2007, and spans approximately 28,000 entries. The volume of data provided in this dataset
introduces an opportunity to derive significant insights into food security studies within this region. The
Palestine food price dataset consists of 14 attributes, which include date (on a monthly frequency), district
(West Bank & Gaza), city, geographical locations (latitude and longitude), commodity category, commodity
item, unit, and price, among others. Samples of prices were collected from twelve distinct cities in the
West Bank and Gaza. The dataset comprises commodities that are categorized into eight groups, with a total
of 39 categories within the commodity type. The dataset was cleaned and preprocessed prior to analysis. This
process involved dealing with missing data, transforming attribute types, and normalizing certain variables.
2.2. Feature engineering
Time series datasets distinguish themselves from other types of datasets mainly due to their
inclusion of a temporal component (time), which introduces an extra dimension to the analysis [42]. Unlike
typical datasets where data points might be independent, time series data is sequential, and each point often
has a relationship with its preceding and sometimes succeeding points. The temporal nature of such datasets
makes them particularly challenging to analyze and model, especially when the goal is to forecast future
values. Additionally, due to the inherent characteristics of time series data like autocorrelation, the risk of
misleading interpretations increases if the analysis isn't handled properly. To address these challenges and
effectively capture underlying patterns, it's essential to apply certain transformations. These transformations
not only assist in identifying trends and seasonality but also in reducing the impact of noise and outliers. By
enhancing each data point with rich information from prior periods, models can be trained to make more
accurate and informed predictions, ultimately leading to more insightful and actionable results [43].
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2.2.1. Moving average
A moving average, also known as a rolling or running average, is a technique often used in time-
series data analysis to smooth out short-term fluctuations and highlight long-term trends or cycles [44]. The
idea is to calculate the average of a particular subset of numbers, and as new data comes in, recalculate that
average by moving the subset window forward as described in (1).
Simple moving average (SMA) =
∑ 𝑃𝑖
𝑁
𝑖=1
𝑁
(1)
Where Pi is the rice of the ith
period, N is the total number of time periods.
On the other hand, the exponential moving average (EMA) is a type of moving average that places
more weight and importance on the most recent data points while still considering the historical data [45].
This makes the EMA more responsive to recent price changes compared to the simple moving average
(SMA), which gives equal weight to all data points as shown in (2).
Exponential moving average, (EMA) = (𝐶 − 𝑃) ×
2
𝑁+1
+ 𝑃 (2)
Where: C is the current data point price, P is the exponential moving average of the previous period, and N is
the total number of time periods. * Simple moving average applied for the first period.
In this study, we found that the simple moving average worked better when we used it on four
different time periods: 6 months, 12 months, 18 months, and 24 months as illustrated in Figure 2. The
superior performance of the simple moving average over the four distinct time periods can be attributed to
several reasons. Firstly, commodity prices often follow cyclical patterns and trends, and the simple moving
average, being a trend-following method, can effectively smooth out short-term fluctuations, capturing the
underlying price movement. Secondly, using multiple time periods allows capturing varying dynamics of the
market, from short-term to more extended cycles, providing a more comprehensive insight.
Figure 2. Simple moving average of five window sizes
Results in Figure 2 display four simple moving averages (SMAs) corresponding to window sizes of
6, 12, 18 and 24 months duration respectively. Each SMA line shows the average price of the asset over its
respective time frame, providing insights into short, medium, and long-term trends. The 6-month SMA reacts
quickly to recent price changes, while the 24-month SMA offers a smoother, long-term perspective,
identifying overall trends and potential turning points. Incorporating these different SMAs as features can
enhance the proposed LSTM model's ability to capture various time horizons in the data. It allows the LSTM
to consider both short-term patterns and long-term trends, improving the accuracy and robustness of the
predictions.
2.2.2. Volatility
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Price volatility refers to the rate at which the price of an asset, such as a stock or commodity,
increases or decreases. It is a statistical measure of the range of the change for a given market index. High
volatility indicates that the commodity's price can change significantly in a short time frame in any direction,
whereas low volatility implies that the price remains steady [46].
Volatility is often calculated using the standard deviation or variance between returns from the same
market index. The most used method is the standard deviation which is typically calculated as illustrated in
(3), where n is the number of price returns used in the calculation, x represents each individual price return, μ
is the average (mean) of the price returns, Σ denotes the sum of the squared differences.
Volatility = √
∑ (𝑥 − 𝜇)2
𝑛
𝑖=1
𝑛
(3)
The price volatilities for each data point were calculated over five different time frames, including
three months (quarterly), six months (semi-annually), one year, two years, and five years. This analysis aids
the model in predicting future trends. When there is significant volatility during a certain period, the model is
more likely to forecast a greater probability of significant price fluctuations as illustrated in Figure 3.
Figure 3. Price volatility over a 3-month window size
Results in Figure 3 show the volatility of a price over a 3-month window size. It shows a line graph
with the x-axis representing time and the y-axis representing the calculated volatility of the asset's price at
each data point. Volatility measures the level of price fluctuation; higher volatility indicates greater price
variability. The chart's line illustrates how the volatility changes over time, highlighting periods of higher and
lower price instability. In the task of price forecasting using LSTM models, these volatility lines play a
crucial role. LSTM models can incorporate volatility as a feature, helping to capture market dynamics and
improve forecasting accuracy. High volatility periods can indicate potential market disruptions or significant
price movements, which LSTM models can leverage to generate more accurate predictions.
2.3. Long short-term memory (LSTM): An overview
Long short-term memory (LSTM) is a variation of recurrent neural network (RNN) architecture,
designed to model temporal sequences and their long-range dependencies more accurately than vanilla
RNNs. It was proposed by Hochreiter and Schmidhuber [32] in 1997. The key to LSTM is the cell state. This
is a kind of "conveyor belt" that carries information across time steps with only minimal changes, which
helps to mitigate the vanishing gradient problem faced by traditional RNNs [47]. In LSTMs, the information
flows through a mechanism that is controlled by various gates as illustrated in Figure 4. These gates decide
what information should be kept or discarded at each time step.
− Forget gate: This gate decides which piece of info should be kept or thrown away. Input is passed through
this gate, which processes it using a sigmoid function.
− Input gate: The input gate produces a new cell’s state via utilizing a sigmoid function that decides which
part to be updated and a tanh which creates a new candidate vector.
− Output gate: This gate produces the next hidden state to the cell that embedded info about the previous
input. This hidden state is crucial for prediction.
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Figure 4. Single-layer LSTM network used for sequence modeling [34]
2.4. Model architecture and training loop
The primary aim of this study is to utilize sophisticated feature engineering methods to build a
straightforward yet efficient deep learning model. The LSTM model architecture constructed 77 input layers,
and 4 hidden layers, succeeded by a fully connected neural network to combine the spatial data from the
surrounding station's layer, which takes 4 inputs from the final hidden layer. Lastly, the rectified linear unit
(ReLU) function is applied to prevent the model from predicting negative values. This structure excels due to
the advanced transformations applied to the data prior to the training. The training was carried out on a local
MAC laptop equipped with 16GB memory and an 8-Core Intel processor. Remarkably, the training time for
the model on the specified dataset was a few minutes, which is a significant time reduction compared to the
computational resources of other researchers which usually take hours as other researchers reported in [33]-
[34]. The LSTM model was implemented using the pytorch deep learning library. Three categorical variables
(city, category, commodity) were encoded using one hot encoding. The numerical variables were normalized
using MinMaxScaler to ensure they were in the same range. Lastly, the dataset was divided into three
subsets: training, evaluation, and testing, with ratios of 70%, 15%, and 15% respectively as illustrated in
Figure 5.
Figure 5. The dataset splits into three subsets: training, evaluation, and testing
Model hyperparameters play a vital role in determining the quality of results obtained from the
training process. In this particular scenario, the model was trained using a learning rate of 1e-3, striking a
balance between precision and efficiency. To measure the model convergence, the mean squared error (MSE)
was employed as the loss function as shown in (4).
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Mean square error (MSE) =
1
𝑛
∑ (𝑌𝑖 − 𝐹𝑖)2
𝑛
𝑖=1 (4)
Where n is the number of data points, Yi is the observed price, and Fi is the forecasted price.
To optimize the training process, the widely used Adam optimizer [48] was utilized, leveraging its
adaptive learning rate capabilities. The training loop was executed for a substantial number of iterations,
precisely 1000 epochs, ensuring that the model had the opportunity to learn from the data. To prevent
overfitting, two effective early-stopping techniques were employed [49]. These techniques acted as
gatekeepers during the training process, monitoring the model's performance closely. Training would halt if
either there was minimal improvement in the training loss; precisely 1/1000 of the learning rate or if the
validation loss increased for two successive epochs. By incorporating these strategies, as described in
Table 1, the training process became more robust and resilient to overfitting, ultimately leading to a more
accurate model.
Table 1. Model hyperparameters
Parameter Value
Epoch 1000
Learning rate 1E-3
Loss function MSE
Optimizer Adam
Regularization Early stop
2.5. Model evaluation
The performance of the proposed model was assessed using three evaluation metrics described in (5)
to (7) and those are: root mean squared error (RMSE), Mean absolute percentage error (MAPE), and the
coefficient of determination (R2
). RMSE measures the square root of the average of the squared differences
between predicted and actual values, providing a measure of the model's accuracy. MAPE calculates the
average percentage difference between predicted and actual values, offering insights into the model's relative
performance. Lastly, the coefficient of determination (R2) indicates the proportion of the variance in the
dependent variable that is predictable from the independent variable, indicating the model's ability to explain
the data's variability. In equation (5)-(7), Ai and Fi are the actual and forecasted prices, respectively, µy is the
mean price of all data points, and n is the number of data points.
RMSE = √
∑ (𝐴𝑖−𝐹𝑖)2
𝑛
𝑖=1
𝑛
(5)
MAPE =
1
𝑛
∑ (
|𝐴𝑖−𝐹𝑖|
𝐴𝑖
)
𝑛
𝑖=1 × 100% (6)
R2
= 1 −
∑ (𝐴𝑖−𝐹𝑖)2
𝑛
𝑖=1
∑ (𝐴𝑖−𝜇𝑦)
2
𝑛
𝑖=1
(7)
3. RESULTS AND DISCUSSION
The utilized dataset consists of eight categories containing thirty-nine distinct commodities. For
training the model, five commodities were selected, resulting in varied but closely related outcomes. The
differences in these outcomes can be attributed to the availability of pricing data for each commodity. Among
the predictions, the estimation of Bread price showed exceptional performance as shown in Figure 6, with a
RMSE of merely 0.14, this suggests that the average difference between the forecasted and actual prices was
exceptionally low, indicating the model's precision in capturing the price fluctuations. Additionally, the
MAPE of 3.04% indicates that, on average, the model's predictions were within a very small percentage of
the actual prices, further substantiating its reliability. Moreover, the high R-squared value of 98.2% denotes
an impressive fit of the model to the observed data, indicating that a significant proportion of the variability
in Bread prices was accurately accounted by the LSTM model. For other commodities i.e. (meat, milk, oil,
and petrol). Table 2 summarizes the evaluations conducted on all of them.
8. Int J Artif Intell ISSN: 2252-8938
Efficient commodity price forecasting using long short-term memory model (Mohammad Tami)
1001
Figure 6. The actual and predicted price of Bread
Table 2. Model evaluation on five commodities
Commodity RMSE MAPE R2
Bread 0.14 3.04% 98.2%
Meat 0.85 4.15% 80%
Milk 1.2 1.09% 93%
Oil (olive) 0.87 2.26% 90%
Petrol 0.22 2.18% 87.4%
The findings in this paper show a notable advancement of the proposed model over earlier published
models. Several previously published models have been compared to our model to offer a thorough
assessment of the efficiency and accuracy of the proposed model in this paper. The advantages of the
proposed model become clear when considering its superior scores across all evaluation metrics.
Furthermore, while many models in the past have shown strength in one or two metrics but weakness in
others, our model maintains consistency in its high performance across RMSE, MAPE, and R2
. This
consistency is indicative of the robustness of our approach, setting a new standard in the field. Table 3
presents a comparison of different predictive models in the literature, evaluating their performance based on
RMSE, MAPE, and R2
metrics.
Table 3. Comparison between results in the literature and the proposed model
Model RMSE*
MAPE*
R2*
Reference
Hybrid (ARIMA + LSTM) 0.15 4.3% - [34]
Stacked LSTM 0.15 0.079% 96.8% [35]
GRU 0.7 - - [38]
S-GAN 0.56 - - [38]
Hybrid (TDNN + ARIMA) 3.35 - - [49]
LSTM 0.14 3.04% 98.2% Our model
* Best result
The models presented in Table 3 showcase various methods, including hybrid approaches, recurrent
neural networks (RNNs), and generative models. The first model, a hybrid of ARIMA and LSTM, achieves
an RMSE of 0.15 and a MAPE of 4.3%. However, the R2
value is missing, making a comprehensive
evaluation challenging. The second model, a stacked LSTM, performs similarly in terms of RMSE (0.15) but
significantly better in MAPE (0.079%) and R2
(96.8%), highlighting its superiority over the hybrid model.
The third model based on gated recurrent unit (GRU) presents an RMSE of 0.7 without reporting the MAPE
and R2
values, and this makes the overall performance of the model difficult to be assessed. The fourth model
uses sentiment analysis with a generative adversarial network (S-GAN) and achieves an RMSE of 0.56, but
again, the lack of MAPE and R2
values limits its evaluation. The fifth model, a hybrid of time delays neural
network (TDNN) and ARIMA perform poorly with an RMSE of 3.35, with MAPE and R2
values not
provided for further assessment. Our LSTM model presented herein delivers outstanding results with an
RMSE of 0.14, a MAPE of 3.04%, and an impressive R2
value of 98.2%. These findings demonstrate the
LSTM model's superior predictive accuracy and its ability to capture underlying patterns effectively. In
conclusion, the comparison of various predictive models highlights the superiority of the stacked LSTM and
the LSTM model presented in this paper. Both models exhibit remarkable performance with low RMSE, low
9. ISSN: 2252-8938
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1002
MAPE, and high R2
values, showcasing the efficacy of LSTM-based architectures for the predictive task.
Notably, the LSTM model presented in this paper stands out due to its simple design and effective feature
engineering techniques. Despite its simplicity, our model demonstrates outstanding predictive accuracy,
surpassing even the stacked LSTM in some metrics. This is due to the emphasis on feature engineering that
has likely contributed to the model's ability to capture essential patterns in the data, leading to superior
predictions. Moreover, one of the key advantages of the LSTM model presented in this paper is its ability to
achieve such high performance with relatively low computation power and training time. This is crucial in
practical applications where computational resources and time are essential. The model's efficiency in
training and inference makes it an attractive choice for real-time predictions and large-scale deployments.
4. CONCLUSION
The study presented in this paper focuses on predicting essential commodity prices using an LSTM
model enhanced by feature engineering. Our results indicate that this approach yields competitive
performance compared to other models found in the literature. The proposed model demonstrated superior
performance, particularly 0.14, 3.04, and 98.2% in RMSE, MAPE, and R2
respectively. The simplicity and
computational efficiency of the proposed model make it a promising approach for commodity price
forecasting, especially in scenarios where computational resources may be limited. Future work could
explore several directions. For instance, future models could incorporate external factors into the price
prediction model. This could be achieved by using natural language processing techniques to analyze news
headlines and social media sentiment related to the commodities under consideration, or by integrating
economic indicators and climate data. Further research could also explore the use of other deep learning
architectures for commodity price prediction. Hybrid models combining the strengths of different
architectures could be particularly promising.
ACKNOWLEDGEMENTS
The authors would like to thank the Arab American University and the editors and reviewers for
their comments and constructive feedback on the manuscript.
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BIOGRAPHIES OF AUTHORS
Mohammad Tami is a senior software engineer at Apple. Mr. Tami always had a
passion for technology and computer engineering. After completing his undergraduate degree
at A Najah National University, Tami began his career working with various US-based
companies in web-based applications and data-intensive systems. Tami's focus is on
developing innovative solutions for data-intensive applications using advanced techniques in
ML and AI. As he gained more experience in the field, he decided to further his education by
pursuing a master's degree in data science at the Arab American University. As a dedicated
and talented computer engineer, Mr. Tami constantly seeks new challenges and opportunities
to further his knowledge and skills. He can be contacted using the following emails:
m.abutami@student.aaup.edu or mabutame@gmail.com.
Amani Yousef Owda assistant professor in Computer Engineering and Data
Science in the Faculty of Graduate Studies at the Arab American University in Palestine. She
worked as a research associate in the Faculty of Engineering at the University of Manchester
from 2019 -2020. In addition, she worked in the School of Engineering at Manchester
Metropolitan University from 2015 - 2019. She worked at Birzeit University from 2007- 2011.
She received her MSc. degree (Hons.) from The University of Manchester, UK in 2013, and
her Ph.D. degree in computer engineering from Manchester Metropolitan University, UK in
2018. She is currently an assistant professor in the Department of Natural, Engineering, and
Technology Sciences, at the Arab American University in Palestine. Since 2018, she leads
research in multi-disciplinary fields with a focus on artificial intelligence, machine learning,
decision support systems, image processing, medical applications of microwave and
millimeter-wave imaging, security screening, and anomaly detection. She has published more
than 42 articles in well reputable journals. She is a reviewer in many well-known Journals, and
she is supervising MSc and PhD students. She can be contacted using the following emails:
Amani.Owda@aaup.edu or amaniabubaha@gmail.com.