Search Results (2,445)

In the context of climate change, studying changes in rainfall patterns is a crucial area of research, remarkably so in arid and semi-arid regions due to the susceptibility of human activities to extreme events such as droughts. Employing predictive models to calculate drought indices can be a useful method for the effective characterization of drought conditions. This study applies two type of machine learning methods—long short-term memory (LSTM) and Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS)—to develop and deploy artificial neural network models with the aim of predicting the regional standardized precipitation index (SPI) in four regions of Zacatecas, Mexico. The predictor variables were a set of climatological time series data spanning from 1964 to 2020. The results suggest that the N-HiTS model outperforms the LSTM model in the prediction and forecasting of SPI time series for all regions in terms of performance metrics: the Mean Squared Error, Mean Absolute Error, Coefficient of Determination and ξ correlation coefficient range from 0.0455 to 0.5472, from 0.1696 to 0.6661, from 0.9162 to 0.9684 and from 0.9222 to 0.9368, respectively, for the regions under study. Consequently, the outcomes revealed the successful performance of the N-HiTS models in accurately predicting the SPI across the four examined regions. Full article

Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the Lower Scheldt Estuary, Belgium. Mutual information (MI) and conditional mutual information (CMI) are used to select optimal driving forces (DFs), with the daily discharge (Q), daily water temperature (WT), and daily sea level (SL) selected as the main DFs. Next, we analyze whether applying a discrete wavelet transform (DWT) to remove the noise from the original time series improves the results. Here, the DWT is applied in Signal-hybrid (SH) and Within-hybrid (WH) frameworks. Both the MLR and ANN models demonstrate satisfactory performance in daily overall salinity simulation over the Scheldt Estuary. The relatively complex ANN models outperform MLR because of their capabilities of capturing complex interactions. Because the nonlinear relationship between salinity and DFs is variable at different locations, the performance of the MLR models in the midstream region is far inferior to that in the downstream region during spring and winter. The results reveal that the application of DWT enhances simulation of both overall and high salinity in this region, especially for the ANN model with the WH framework. With the effect of Q decline or SL rise, the salinity in the middle Scheldt Estuary increases more significantly, and the ANN models are more sensitive to these perturbations. Full article

This study showcased the Markov switching autoregressive model with time-varying parameters (MSAR-TVP) for modeling nonlinear time series with structural changes. This model enhances the MSAR framework by allowing dynamic parameter adjustments over time. Parameter estimation uses maximum likelihood estimation (MLE) enhanced by the Kim filter, which integrates the Kalman filter, Hamilton filter, and Kim collapsing, further refined by the Nelder–Mead optimization technique. The model was evaluated using U.S. real gross national product (GNP) data in both in-sample and out-of-sample contexts, as well as an extended dataset to demonstrate its forecasting effectiveness. The results show that the MSAR-TVP model improves forecasting accuracy, outperforming the traditional MSAR model for real GNP. It consistently excels in forecasting error metrics, achieving lower mean absolute percentage error (MAPE) and mean absolute error (MAE) values, indicating superior predictive precision. The model demonstrated robustness and accuracy in predicting future economic trends, confirming its utility in various forecasting applications. These findings have significant implications for sustainable economic growth, highlighting the importance of advanced forecasting models for informed economic policy and strategic planning. Full article

Ship trajectory prediction is a complex time series forecasting problem that necessitates models capable of accurately capturing both long-term trends and short-term fluctuations in vessel movements. While existing deep learning models excel in short-term predictions, they struggle with long-sequence time series forecasting (LSTF) due to difficulties in capturing long-term dependencies, resulting in significant prediction errors. This paper proposes the Informer-TP method, leveraging Automatic Identification System (AIS) data and based on the Informer model, to enhance the ability to capture long-term dependencies, thereby improving the accuracy of long-term ship trajectory predictions. Firstly, AIS data are preprocessed and divided into trajectory segments. Secondly, the time series is separated from the trajectory data in each segment and input into the model. The Informer model is utilized to improve long-term ship trajectory prediction ability, and the output mechanism is adjusted to enable predictions for each segment. Finally, the proposed model’s effectiveness is validated through comparisons with baseline models, and the influence of various sequence lengths $L_{token}$ on the Informer-TP model is explored. Experimental results show that compared with other models, the proposed model exhibits the lowest Mean Squared Error, Mean Absolute Error, and Haversine distance in long-term forecasting, demonstrating that the model can effectively capture long-term dependencies in the trajectories, thereby improving the accuracy of long-term vessel trajectory predictions. This provides an effective and feasible method for ensuring ship navigation safety and advancing intelligent shipping. Full article

In line with several European directives, residents are strongly encouraged to invest in renewable power plants and flexible consumption systems, enabling them to share energy within their Renewable Energy Community at lower procurement costs. This, along with the ability for residents to switch between such communities on a daily basis, leads to dynamic portfolios, resulting in non-stationary and discontinuous electrical load time series. Given poor predictability as well as insufficient examination of such characteristics, and the critical importance of electrical load forecasting in energy management systems, we propose a novel forecasting framework using Federated Learning to leverage information from multiple distributed communities, enabling the learning of domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we evaluate their effectiveness by applying different tests for white noise in the forecast error signal. The findings suggest that our proposed framework is capable of effectively forecast non-stationary as well as discontinuous time series, extract domain-invariant features, and is applicable to new, unseen data through the integration of knowledge from multiple sources. Full article

The literature on discrete valued time series is expanding very fast. Very often we see new models with very similar properties to the existing ones. A natural question that arises is whether the multitude of models with very similar properties can really have a practical purpose or if they mostly present theoretical interest. In the present paper, we consider four models that have negative binomial marginal distributions and are autoregressive in order 1 behavior, but they have a very different generating mechanism. Then we try to answer the question whether we can distinguish between them with real data. Extensive simulations show that while the differences are small, we still can discriminate between the models with relatively moderate sample sizes. However, the mean forecasts are expected to be almost identical for all models. Full article

We propose a novel approach for time series forecasting with many predictors, referred to as the GO-sdPCA, in this paper. The approach employs a variable selection method known as the group orthogonal greedy algorithm and the high-dimensional Akaike information criterion to mitigate the impact of irrelevant predictors. Moreover, a novel technique, called peeling, is used to boost the variable selection procedure so that many factor-relevant predictors can be included in prediction. Finally, the supervised dynamic principal component analysis (sdPCA) method is adopted to account for the dynamic information in factor recovery. In simulation studies, we found that the proposed method adapts well to unknown degrees of sparsity and factor strength, which results in good performance, even when the number of relevant predictors is large compared to the sample size. Applying to economic and environmental studies, the proposed method consistently performs well compared to some commonly used benchmarks in one-step-ahead out-sample forecasts. Full article

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model’s capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. Full article

Accurate forecasting of inbound visitor numbers is crucial for effective planning and resource allocation in the tourism industry. Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as economic conditions. Regression models, on the other hand, face challenges when dealing with high-dimensional data. Previous autoencoders for feature selection do not simultaneously incorporate feature and target information simultaneously, potentially limiting their effectiveness in improving predictive performance. This study presents a novel approach that combines a target-concatenated autoencoder (TCA) with ensemble learning to enhance the accuracy of tourism demand predictions. The TCA method integrates the prediction target into the training process, ensuring that the learned feature representations are optimized for specific forecasting tasks. Extensive experiments conducted on the Taiwan and Hawaii datasets demonstrate that the proposed TCA method significantly outperforms traditional feature selection techniques and other advanced algorithms in terms of the mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R²). The results show that TCA combined with XGBoost achieves MAPE values of 3.3947% and 4.0059% for the Taiwan and Hawaii datasets, respectively, indicating substantial improvements over existing methods. Additionally, the proposed approach yields better R² and MAE metrics than existing methods, further demonstrating its effectiveness. This study highlights the potential of TCA in providing reliable and accurate forecasts, thereby supporting strategic planning, infrastructure development, and sustainable growth in the tourism sector. Future research is advised to explore real-time data integration, expanded feature sets, and hybrid modeling approaches to further enhance the capabilities of the proposed framework. Full article

Energy is a foundational element of the modern industrial economy. Prices of metals play a crucial role in energy sectors’ revenue evaluations, making them the cornerstone of effective payment management employed by resource policymakers. Copper is one of the most important industrial metals, and plays a vital role in various aspects of today’s economies. Copper is strongly associated with many industries, such as electrical wiring, construction, and equipment manufacturing; therefore, the price of copper has become a significant impact factor on the performance of related energy companies and economies. The accurate prediction of copper prices holds particular significance for market participants and policymakers. This study carried out research to address the gap in copper price forecasting using a one-dimensional convolutional neural network (1D-CNN). The proposed method was implemented and tested using extensive data spanning from November 1991 to May 2023. To assess the performance of the CNN model, standard evaluation metrics, such as the R-value, mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), were employed. For the prediction of global copper prices, the proposed artificial intelligence algorithm demonstrated high accuracy. Lastly, future global copper prices were predicted up to 2027 by the CNN and compared with forecasts published by the International Monetary Fund and the International Society of Automation. The results show the exceptional performance of the CNN, establishing it as a reliable tool for monitoring copper prices and predicting global copper price volatilities near reality, and as carrying significant implications for policymakers and governments in shaping energy policies and ensuring equitable implementation of energy strategies. Full article

By exploiting the patterns in past data points, we could forecast long-term consumption with a computationally simple algorithm. Our approach is simple to interpret. It incorporates the seasonality of past consumption and can predict power consumption for any time scale. The algorithm can be easily implemented directly in SQL. It can run sub-second long-term predictions on large-scale data marts. The proposed method scored a Mean Absolute Percentage Error (MAPE) of just 5.88% when predicting hourly values for France’s electric consumption in 2017 based on hourly data from 2008 to 2011. Full article

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east–west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities. Full article

Reservoir impoundment significantly impacts the hydrogeological conditions of reservoir bank slopes, and bank slope deformation or destruction occurs frequently under cyclic impoundment conditions. Ground deformation prediction is crucial to the early warning system for slow-moving landslides. Deep learning methods have developed rapidly in recent years, but only a few studies are on combining deep learning and landslide warning. This paper proposes a slow-moving landslide displacement prediction method based on the Informer deep learning model. Firstly, the Sentinel-1 (S1) data are processed to obtain the cumulative displacement time-series image of the bank slope by the Small-BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) method. Then, combining data on rainfall, humidity, and horizontal and vertical distances of pixel points from the water table line, this study created a dataset with landslide displacement as the target feature. After that, this paper improves the Informer model to make it applicable to our dataset. This study chose the Dawanzi landslide in the Baihetan reservoir area, China, for validation. After training with 50-time series deformation data points, the model can predict the displacement results of 12-time series deformation data points using 12-time series multi-feature data, and compared with the monitoring values, its Mean Square Error (MSE) was 11.614. The results show that the multivariate dataset is better than the deformation univariate data in predicting the displacement in the large deformation zone of bank slopes, and our model has better complexity and prediction performance than other deep learning models. The prediction results show that among zones I–IV, where the Dawanzi Tunnel is located, significant deformation with the maximum deformation rate detected exceeding –100mm/year occurs in Zones I and III. In these two zones, the initiation of deformation relates to the drop in water level after water storage, with the deformation rate of Zone III exhibiting a stronger correlation with the change in water level. It is expected that deformation in Zone III will either remain slow or stop, while deformation in Zone I will continue at the same or a decreased rate. Our proposed method for slow-moving landslide displacement forecasting offers fast, intuitive, and economically feasible advantages. It can provide a feasible research idea for future deep learning and landslide warning research. Full article

Time series forecasting has played an important role in different industries, including economics, energy, weather, and healthcare. RNN-based methods have shown promising potential due to their strong ability to model the interaction of time and variables. However, they are prone to gradient issues like gradient explosion and vanishing gradients. And the prediction accuracy is not high. To address the above issues, this paper proposes a Fractional-order Lipschitz Recurrent Neural Network with a Frequency-domain Gated Attention mechanism (FLRNN-FGA). There are three major components: the Fractional-order Lipschitz Recurrent Neural Network (FLRNN), frequency module, and gated attention mechanism. In the FLRNN, fractional-order integration is employed to describe the dynamic systems accurately. It can capture long-term dependencies and improve prediction accuracy. Lipschitz weight matrices are applied to alleviate the gradient issues. In the frequency module, temporal data are transformed into the frequency domain by Fourier transform. Frequency domain processing can reduce the computational complexity of the model. In the gated attention mechanism, the gated structure can regulate attention information transmission to reduce the number of model parameters. Extensive experimental results on five real-world benchmark datasets demonstrate the effectiveness of FLRNN-FGA compared with the state-of-the-art methods. Full article

We introduce the function principal component regression (FPCR) forecasting method to model and forecast age-specific survival functions observed over time. The age distribution of survival functions is an example of constrained data whose values lie within a unit interval. Because of the constraint, such data do not reside in a linear vector space. A natural way to deal with such a constraint is through an invertible logit transformation that maps constrained onto unconstrained data in a linear space. With a time series of unconstrained data, we apply a functional time-series forecasting method to produce point and interval forecasts. The forecasts are then converted back to the original scale via the inverse logit transformation. Using the age- and sex-specific survival functions for Australia, we investigate the point and interval forecast accuracies for various horizons. We conclude that the functional principal component regression (FPCR) provides better forecast accuracy than the Lee–Carter (LC) method. Therefore, we apply FPCR to calculate annuity pricing and compare it with the market annuity price. Full article