Autoregressive Integrated Moving Average

Population growth is the main challenge for the total Cultivable/Agricultural Land in India. The Box-Jenkins model is used in this study to forecast the total Cultivable/Agricultural Land in India. This study is based on the data of total cultivable land in India, covering from 2050-51 to 2017-18 of the Ministry of Agriculture and Farmers Welfare, Government of India. The diagnostic check reveals that ARIMA (2, 1, 0) is the best model for forecasting total Cultivable/Agricultural Land in India. To know the future demand of Cultivable/Agricultural Land forecasting is very important. In this paper, we forecasted the total Cultivable/Agricultural Land in India for the upcoming 10 years (up to 2028).

Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate results than conventional regression-based modeling. It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such as Long Short-Term Memory (LSTM), are superior compared to Autoregressive Integrated Moving Average (ARIMA) with a large margin. The LSTM-based models incorporate additional "gates" for the purpose of memorizing longer sequences of input data. The major question is that whether the gates incorporated in the LSTM architecture already offers a good prediction and whether additional training of data would be necessary to further improve the prediction. Bidirectional LSTMs (BiLSTMs) enable additional training by traversing the input data twice (i.e., 1) left-to-right, and 2) right-to-left). The research question of interest is then whether BiLSTM, with additional ...

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

Nigeria has been faced with the macroeconomic problem of inflation for a long period of time. The problem slows down the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in Africa including Nigeria. Therefore, forecasting inflation rates in Nigeria becomes very important for the government to design economic strategies or effective monetary policies to combat any unexpected high inflation in the country. This study utilizes seasonal autoregressive integrated moving average model (SARIMA) to forecast inflation rates in Nigeria using monthly inflation data from January 1999 to December 2018. We discover that SARIMA (1, 1, 1) × (0, 0, 1)12 can represent very well in the data behavior and the forecasting of inflation rate in Nigeria. The research revealed that the inflation of Nigeria is non-stationary and based on the selected model, we forecast twelve (12) months inflation rates of Ni...

Accurate rainfall forecasting in watersheds is of indispensable importance for predicting streamflow and flash floods. This paper investigates the accuracy of several forecasting technologies based on Wavelet Packet Decomposition (WPD) in monthly rainfall forecasting. First, WPD decomposes the observed monthly rainfall data into several subcomponents. Then, three data-based models, namely Back-propagation Neural Network (BPNN) model, group method of data handing (GMDH) model, and autoregressive integrated moving average (ARIMA) model, are utilized to complete the prediction of the decomposed monthly rainfall series, respectively. Finally, the ensemble prediction result of the model is formulated by summing the outputs of all submodules. Meanwhile, these six models are employed for benchmark comparison to study the prediction performance of these conjunction methods, which are BPNN, WPD-BPNN, GMDH, WPD-GMDH, ARIMA, and WPD-ARIMA models. The paper takes monthly data from Luoning and Zuoyu stations in Luoyang city of China as the case study. The performance of these conjunction methods is tested by four quantitative indexes. Results show that WPD can efficiently improve the forecasting accuracy and the proposed WPD-BPNN model can achieve better prediction results. It is concluded that the hybrid forecast model is a very efficient tool to improve the accuracy of mid-and long-term rainfall forecasting.

A univariate time-series analysis method has been used to model and forecast the monthly number of dengue haemorrhagic fever (DHF) cases in southern Thailand. We developed autoregressive integrated moving average (ARIMA) models on the data collected between 1994-2005 and then validated the models using the data collected between January-August 2006. The results showed that the regressive forecast curves were consistent

The linear Gaussian state space model for which the common variance is treated as a stochastic time-varying variable is considered for the modelling of economic time series. The focus of this paper is on the simultaneous estimation of parameters related to the stochastic processes of the mean part and the variance part of the model. The estimation method is based on maximum likelihood and it requires the subsequent uses of the Kalman filter to treat the mean part and sampling techniques to treat the variance part. This approach leads to the evaluation of the exact likelihood function of the model subject to simulation error. The standard asymptotic properties of maximum likelihood estimators apply as a result. A Monte Carlo study is carried out to investigate the small-sample properties of the estimation procedure. We present two illustrations which are concerned with the modelling and forecasting of two U.S. macroeconomic time series: inflation and industrial production.

Pada peramalan terdapat sebuah ketidakpastian atau error karena sifat peramalan yang masih perkiraan sementara. Maka dari itu dari semua metode peramalan ada beberapa pilihan metode permalan yang dapat digunakan dengan kesalahan peramalan yang rendah, salah satunya adalah ARIMA. Metode peramalan tersebut terdapat sebuah ketidakpastian dalam proses penentuan parameter metode ARIMA. Kombinasi nilai pada parameter tersebut haruslah dicoba terus-menerus hingga didapatkan nilai peramalan yang optimal. Berangkat dari permasalahan tersebut, maka dibuatlah semacam sistem pendukung dari proses ARIMA itu sendiri yang otomatis dalam menentukan parameter untuk sebuah data secara optimal dengan menggunakan Algoritma Genetika. Hasil dari penelitian ini berupa sebuah sistem yang dapat menentukan nilai parameter pada metode peramalan ARIMA secara optimal yang mana hasil tersebut akan digunakan dalam penentuan produksi oleh perusahaan tersebut. Sehingga sistem tersebut dapat menunjang dalam peramalan pada perusahaan berdasarkan pola data yang sudah ada dengan optimal. Abstract Uncertainty or forecasting error due to the nature of which is still hypothesis. Therefore from all methods of forecasting there are several options forecasting methods that can be used with a low forecasting errors, one of them is ARIMA. The forecasting method there is an uncertainty in the process of determining the parameters of ARIMA method. The combination of value in the parameter must be tested continuously to obtain the optimal forecasting value. Departing from these problems, then made some sort of support system of ARIMA process itself is automatic in determining the parameters for an optimal data using Genetic Algorithms. Results from this study in the form of a system that can determine the value of a parameter in an optimal ARIMA forecasting method in which these results will be used in determining the production by these companies. So that the system can support in forecasting the company based on the pattern of existing data to the optimum. Permintaan produk di perusahaan dipengaruhi oleh beberapa faktor, yaitu jumlah penduduk dan pendapatan per kapita. Sedangkan permasalahan yang dihadapi adalah permintaan yang tidak menentu sehingga seringnya terjadi kesalahan dalam penentuan jumlah produksi. Berdasarkan permasalahan diatas, pemecahan masalah yang ditawarkan adalah dengan menggunakan peramalan dengan metode ARIMA. Metode ini dipilih karena dapat digunakan untuk manganalisis situasi yang acak, tren, musim bahkan sifat siklis. Tetapi ada sebuah ketidakpastian dalam proses peramalannya yaitu terletak pada penentuan p, d, dan q yang merupakan parameter dari metode ARIMA yang akan digunakan sebagai solusi permasalahan diatas. Untuk mengatasi hal tersebut, maka Algoritma Genetika dapat dijadikan solusi dari permasalahan pencarian parameter tersebut. Algoritma Genetika dipilih karena dapat menemukan hasil terbaik dari semua kemungkinan percobaan pencarian parameter. Atas dasar kelebihan-kelebihan serta faktor-faktor yang mempengaruhi dari pola data pada sebuah perusahaan, maka dalam penelitian ini Algoritma Genetika tersebut digunakan sebagai solusi untuk memecahkan masalah optimalisasi nilai parameter pada metode ARIMA.

Increasingly, wind electric generation is becoming indispensable to complement the global energy matrix. In this way, it is necessary to develop forecasting methods for this type of renewable resource, in order to guarantee the appropriate use to be compatible with the needs of the electricity sector. In this paper, two hybrid models are proposed that combine Autoregressive Integrated Moving Average methodologies (ARIMA) with that of artificial neural networks (ANNs) to forecast time series of daily average wind speeds from data collected at 10 meters. The first model is a nonlinear autoregressive neural network, NARNET, of order (3,3,1) and the second a NARNET of order (4,3,1). The training of the models occurred with data collected from two cities in the northeast of Brazil, Acaraú and Guaramiranga, during a period of 100 days and the forecast occurred for a period of 126 consecutive days after the training. The forecast horizon is average, around days for 3 months. Moreover, the choice of the best forecasting model occurred through the analysis of statistical accuracy indices, among which are: the mean absolute error (MAE), the mean square error (MSE), the mean absolute percentage error (MAPE), the model suitability (FIT) and the correlation coefficient (R) and of determination (R 2). The forecast proved to be efficient in the localities of Acaraú with MSE errors of 0,2118 m/s, MAPE 10,75%, R 0,8685 and also for the locality of Guaramiranga MSE 0,2234 m/s, MAPE 12,16% and R 0,7233. In addition, the forecast curves for both locations showed good agreement with the observed values. Finally, the possibility of further improving hybrid modeling is discussed by changing the activation functions of the hidden layer.

Forecasting time series data is an important subject in economics, business, and finance. Traditionally, there are several techniques to effectively forecast the next lag of time series data such as univariate Autoregressive (AR), univariate Moving Average (MA), Simple Exponential Smoothing (SES), and more notably Autoregressive Integrated Moving Average (ARIMA) with its many variations. In particular, ARIMA model has demonstrated its outperformance in precision and accuracy of predicting the next lags of time series. With the recent advancement in computational power of computers and more importantly developing more advanced machine learning algorithms and approaches such as deep learning, new algorithms are developed to forecast time series data. The research question investigated in this article is that whether and how the newly developed deep learning-based algorithms for forecasting time series data, such as "Long Short-Term Memory (LSTM)", are superior to the traditi...

Uncertainty or forecasting error due to the nature of which is still hypothesis. Therefore from all methods of forecasting there are several options forecasting methods that can be used with a low forecasting errors, one of them is ARIMA. The forecasting method there is an uncertainty in the process of determining the parameters of ARIMA method. The combination of value in the parameter must be tested continuously to obtain the optimal forecasting value. Departing from these problems, then made some sort of support system of ARIMA process itself is automatic in determining the parameters for an optimal data using Genetic Algorithms. Results from this study in the form of a system that can determine the value of a parameter in an optimal ARIMA forecasting method in which these results will be used in determining the production by these companies. So that the system can support in forecasting the company based on the pattern of existing data to the optimum.

Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holt’s linear exponential smoothing, and the autoregressive conditional heteroscedastic (with their extensions to generalized autoregressive conditional heteroscedastic) models were fitted to the data. The goodness of fit is performed via the Akaike information criteria, Schwartz Bayesian criteria. It was established that the generalized autoregressive conditional heteroscedastic model was superior to the autoregressive integrated moving average model and Holt’s linear exponential smoothing because the data was characterized by changing mean and variance.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11486 Dhaka Univ. J. Sci. 60(2): 159-162, 2012 (July)

Electricity is among the most crucial needs for every people in this world. It is defined by the set of physical phenomena related with the flow of electrical charge. The importance of electricity itself leads to the increasing electricity load demand in the world including Malaysia. The purpose of the current study is to evaluate the performance of combined ARIMA with Regression model in forecasting electricity load demand in Johor Bahru. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Regression models will be used as benchmark models since the model has been proven in many forecasting context. Using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as a forecasting accuracy criteria, the study concludes that the combined method is more appropriate model.

This paper attempts to compare the forecasting performance of the ARIMA model and hybrid ARMA-GARCH Models by using daily data of the Iran’s exchange rate against the U.S. Dollar (IRR/USD) for the period of 20 March 2014 to 20 June 2015. The period of 20 March 2014 to 19 April 2015 was used to build the model while remaining data were used to do out of sample forecasting and check the forecasting ability of the model. All the data were collected from central bank of Iran. First of all, the stationary of the exchange rate series is examined using unit root test which showed the series as non stationary. To make the exchange rate series stationary, the exchange rates are transformed to exchange rate returns. By using Box-Jenkins method, the appropriate ARIMA model was obtained and for capturing volatilities of returns series, some hybrid models such as: ARIMA-GARCH, ARIMA-IGARCH, ARIMA-GJR and ARIMA-EGARCH have been estimated. The results indicate that in terms of the lowest RMSE, MAE...

Forecasting of vegetable area, production, and productivity of Nepal was made from the historical data of 1977/78 to 2019/20 by using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models. The best fitted ARIMA models were chosen based on the minimum value of the selection criterion, Akaike information criteria (AIC) and Bayesian information criteria (BIC). ARIMA (0, 2, 1) model was found suitable for all areas and production, whereas ARIMA (0, 2, 0) model was best fitted for forecasting vegetable productivity. The model was cross-validated by comparing the point prediction with the actual test series data from 2015/16 to 2019/20. The performances of models were determined from the mean absolute percent error (MAPE) value. The MAPE was found to be 2.70%, 2.40%, and 3.80%, respectively for the prediction of area, production, and productivity. The forecast was made for the immediate five years (2020/21 to 2024/25), and it showed an increasing value for area and produ...

The monthly exchange rates between the Central African Franc (XAF) and the US Dollar (USD) are herein modeled by SARIMA techniques. The realization from January 1997 to March 2013 (referred to as XDER herein) is analyzed in this work. The time plot of XDER in Figure 1 shows an overall upward secular trend with no obvious regular seasonal component. As expected, XDER is shown to be non-stationary by the Augmented Dickey Fuller (ADF) test. Seasonal (i.e. 12-monthly) differencing of XDER yields the series SDXDER which shows an overall horizontal trend, yet with no definite seasonal movement (See figure 2). The ADF test certifies SDXDER as stationary. Its correlogram in Figure 4 suggests an ARMA(1, 5) model fit. A non-seasonal differencing of SDXDER yields the series DSDXDER which exhibits an overall horizontal trend. No seasonal component is discernible from the visual inspection of its time plot of Figure 3. However its correlogram in Figure 5 has a significant negative spike at lag 12 and comparable spikes at lags 11 and 13, an indication of a 12 monthly seasonality and the presence of a seasonal moving average component of order one. Moreover this is the autocorrelation structure of a (0, 1, 1)x(0, 1, 1) 12 SARIMA model. Hence the two models, (1, 0, 5)x(0, 1, 0) 12 and (0, 1, 1)x(0, 1, 1) 12 , are proposed and fitted. The latter has been shown to be the more adequate.

This study aims to analysis the effect of macroeconomic variables on the overall return of company shares which is a proxy with changes in the composite stock price index. This study uses secondary data in a period of 20 months from November 2016 to June 2018. While the analysis technique uses multiple linear regression This study found that macroeconomic variables consisting of inflation rates, interest rates, money supply, and foreign exchange rates, stock returns have a significant effect on companies on the Indonesia Stock Exchange.

The examination of economic aspects of gas emissions and its consequences is very important, especially in terms of its volume at the current increasing trend. Therefore, the prediction of air pollution emissions of carbon dioxide can give the correct direction to policies adopted. Hence, studying and forecasting of gas emissions is necessary. The purpose of this paper is the prediction of CO 2 emissions based on Grey System and Autoregressive Integrated Moving Average and comparison of these two methods by RMSE, MAE and MAPE metrics. The results show the more accuracy of Grey system forecasting rather than other methods of prediction. Also, based on the estimated results, the amount of carbon dioxide emissions will reach up to 925.68 million tons in 2020 which shows an increase of 66 percent growth compared to 2010 which is highly significant.

Hourly and daily measurements of total and diffuse solar radiation and sunshine duration are analyzed. Three-year measurements of the available meteorological data in Mosul (latitude 36° 19′ N, longitude 43° 09′ E and 223 m above mean sea level) are used in this study. The present work involves two parts; in the first one, monthly mean daily and hourly of total and diffuse solar radiation are analyzed. The results show that the annual mean of the daily total and diffuse solar radiation is 5.11 and 1.6 kWh/m2 respectively. 57% of the days of the year are clear, while only 11.5% of the days are cloudy. Several empirical equations for estimating monthly mean daily global and diffuse solar radiation have been developed and compared with other available models. Ratio of average hourly to daily total solar radiation, for each month of the year is studied and compared with the theoretical results. In the second part, time-series model building using Box–Jenkins procedure to the daily clearness index is performed. The ARIMA(2,1,1) is developed for predication of daily clearness index.

<jats:p>The use of currency exchange estimation as a tool for economic planning is being researched as a technique for gaining economic stability. The main purpose of this study is to use the ARIMA model to forecast monthly US dollar and IQ dinar exchange rates. The information was gathered from January 2010 to December 2020. We got the information from the website (sa.investing.com). The minimum value of Root Mean Square Error (RMSE) and the mean absolute error are used to select the optimal model (MAE). ARIMA was found to be the best model for the US Dollar/IQ Dinar series (2, 1,0). This is the forecasted meaning for the future of this exchange rate time series, which indicates a perpetual increase continuously in the next two years. Statgraphics version 15 was the statistical software package utilized to complete this project.</jats:p>

ABSTRACT: The present study is an attempt to build a Univariate time series model to forecast the exports of industrial goods from Punjab for ensuing decade till 2020. The study employs Box-Jenkin's methodology of building ARIMA (Autoregressive Integrated Moving ...

This paper aims to find a forecasting model based on the comparative study of wavelet- ARIMA and EMD-ARIMA models and residual-based model selection technique for temperature time series. Time series analysis is essential in studying temperature data for investigating the variation and predicting the future trend, in which we can control the changes and make good decisions. And most important is to understand the trend in the series with time. This study applied hybridized models of wavelet transform and empirical mode decomposition with seasonal autoregressive integrated moving average (SARIMA), which combines two models to get better accuracy, for forecasting daily average temperature time series data in the central region of Eritrea, Asmara. Daily data was collected for 30 years, from January 1, 1991, to December 31, 2020. The study compares WT-SARIMA and EMD-SARIMA models to find a well fit and better forecasting model. Model selection techniques are essential for time series an...

The main focus of my research is improving the accuracy of time series forecasting using global ensemble approaches. The forecasting/prediction models that I develop can be used to address many real-world forecasting problems such as sales demand forecasting, energy demand forecasting and weather forecasting.

Currently the emergence of the novel coronavirus (Sars-Cov-2), which causes the COVID-19 pandemic and has become a serious health problem because of the high risk causes of death. Therefore, fast and appropriate action is needed to reduce the spread of the COVID-19 pandemic. One of the way is to build a prediction model so that it can be a reference in taking steps to overcome them. Because of the nature of transmission of this disease which is so fast and massive cause extreme data fluctuations and between objects whose observational distances are far enough correlated with each other (long memory). The result of this determination is the best ARFIMA model obtained to predict additional of recovering cases of COVID-19 is (1,0,489.0) with an SMAPE value of 12,44%, while the case of death is (1.0.429.0) with SMAPE value of 13,52%. This shows that the ARFIMA model can accommodate well the long memory effect, resulting in a small bias. Also in estimating model parameters, it is also si...

Autoregressive Integrated Moving Average processes (ARIMA) of various orders were presented, with a view to identifying optimal model from a class of models. In this paper, parameters of the models were estimated using Ordinary Least Square (OLS) approach. ARIMA (p, d, q) was formulated for maximum daily temperature data of Ondo and Zaira from January 1995 to November 2005. The choice of autoregressive integrated moving average models of orders p and q was intended to retain any persistence in natural process. To determine the performance of models, Normalized Bayesian Information Criteria was adopted. ARIMA (1, 1, 1) was adequate for modelling maximum daily temperature of Ondo and Zaira, parameters of the model were estimated and redundant variable was removed. Causality and invertiblity behaviour of some optimal models are also presented.

The purpose of this study is to apply the α-Sutte Indicator and ARIMA in forecasting data. α-Sutte Indicator is a new forecasting method that was developed in 2017 by Ansari Saleh Ahmar. To see the accuracy of these methods, the forecasting results of the α-Sutte Indicator will be forecasting methods compared to other items, namely: ARIMA. Based on the results of forecasting, it is found that α-Sutte Indicator has MSE and MAE values that are lower than other methods (ARIMA). This is supported by MSE data from α-Sutte Indicator smaller than ARIMA(1,1,1).

Corona virus Disease, a disease which was discovered in December, 2019 has been spreading worldwide like wildfire. In view of this, there is need of continuous findings on the impact, consequence and possible medications of the pandemic in Nigeria and the world at large. Therefore, this research is aimed at Analyzing the spread of Coronavirus pandemic in Nigeria, using univariate and multivariate models namely;(ARIMA) and (ARIMAX). The daily data used in this research was obtained from the NCDC official website dated from 19th April, 2020 to 20th April, 2021 with total of 384 observations using R and Eview10 software for the analysis. Three different variables were examined. The variables are; total confirmed, discharged and death cases for the purpose of establishing reliable forecast, for better decision making and a helping technique for drastic action in reducing the day to day spread of the pandemic. Summary statistics and stationary test were checked with the data being statio...

Meteorological data is required to evaluate the long term effect of proposed man made hydrological changes. Such evaluations are often hydrologic processes which use weather data as input. The meteorological variables required for most of hydrologic models includes precipitation it is essential to have a sufficient long historic data to achieve an operating mathematical rule. Records which consist of short term data are not suitable for proper planning and arrangement processes and also available of a location of interest. in such circumstances, it become necessary to deterministic as well as stochastic component, but stochastic time series models, such as autoregressive (AR), moving average(MA) autoregressive and moving average (ARMA) are widely used to developed and generate the rainfall and temperature. They apply Seasonal ARIMA models that counted for 92% of the total variability in the monthly means of air temperature. Their forecasted values showed good agreement with the actu...

Using annual time series data on net FDI inflows for Nigeria from 1960 to 2017, the study analyzes net FDI inflows using the Box – Jenkins ARIMA methodology. The Augmented Dickey-Fuller tests show that Nigeria FDI net FDI inflows data is I (1). Based on the AIC, the study presents the ARIMA (0, 1, 2) model. The diagnostic tests further show that the presented model is not only stable but also acceptable for explaining net FDI dynamics in Nigeria. The results of the study indicate that net FDI inflows in Nigeria are likely to deteriorate over the next decade. The study offers important policy recommendations in an effort to help policy makers in Nigeria on how to promote and maintain the much needed net FDI inflows in Nigeria.

Tuberculosis (TB) remains the world's deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country's public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (...

Forex or Foreign Exchange is trading a country's currency with another country's currency. The purpose of this study is basically to test the accuracy of ARIMA on the GBP/USD currency pair. In addition, this research is expected to provide the benefits of knowledge about forecasting using ARIMA. This study resulted in forecasting the GBP/USD currency pair within 1 month, per 6 months from January 2018 to June 2018 using the ARIMA method and R software. Data to be used are data taken from January 2013 to June 2018. For the the process will follow the process of the KDD (Knowledge Discovery in Database). The results obtained by the ARIMA model (3,2,1) as the best model to be applied for 1 month per 6 months on the GBP/USD currency pair because it has the lowest AIC value and the mean absolute percentage error is 3.16%.