Search Results (3,845)

Tunisia is characterized by high precipitation variability, which results in frequent extreme floods and droughts. This study aims to develop long-term forecasting models for total and daily maximum annual precipitation by incorporating information related to climate variability. These models use low-frequency climate oscillation indices as predictors. A linear functional model for scalar response is developed for this purpose. The model based on functional data analysis is also compared to a linear regression model. The station under study is located in north-eastern Tunisia. The association between precipitation and four climate indices is evaluated: the North Atlantic Oscillation (NAO), the Pacific Decadal Oscillation (PDO), the Mediterranean Oscillation (MO) and the Western Mediterranean Oscillation (WeMO) climate indices. The results show that both linear and functional regression provide good and comparable results, likely due to the limited length of the data series. NAO, PDO and MO are the best indices to forecast total annual precipitation with an RMSE between 3.564% and 4.151% of the average precipitation, while MO seems to be the best index to forecast daily maximum annual precipitation achieving slightly higher RMSE between 11.174% and 11.916% of the average maximum precipitation. These results suggest that total precipitation at the study station is controlled by large-scale climatic processes operating over the Atlantic, Pacific, and Mediterranean regions, whereas the few most extreme precipitation events are primarily driven by regional climatic phenomena occurring at the Mediterranean scale. The results may have practical applications to improve disaster response preparedness and water resource management. Full article

This study investigates the short-term and long-term impacts of gross domestic product (GDP), inflation, foreign capital flows, trade balance and interest rate on stock market performance in Saudi Arabia for the period 1990–2023. The autoregressive distributed lag (ARDL) approach and error correction model (ECM) are employed to empirically examine the short-run and long-run relationships. The ARDL-ECM technique is effective for analyzing cointegration and assessing adjustment processes. Additionally, impulse response function (IRF) analysis based on the vector autoregression (VAR) model, estimated using these macroeconomic indicators, is applied in this paper. This study provides novel insights and addresses emerging gaps in the literature concerning Saudi Arabia as a developing economy. The long-term relationship in the bounds test results confirms its existence. In the long run, inflation and interest rate exert a statistically significant negative effect on stock market performance, while the trade balance has a significant positive impact. GDP and foreign capital inflows do not exhibit statistically significant long-run effects. Short-run dynamics indicate persistence in stock market performance along with significant effects from inflation and interest rate changes, while GDP and foreign capital inflows remain statistically insignificant in the long-run scenario. Forecast error variance decomposition (FEVD) results show that approximately 68.5% of the variation in market performance is explained by its own shocks, followed by foreign capital flows (16.3%) and inflation (8.4%). While foreign capital flow does not exhibit statistical significance in the ARDL long-run estimates, its contribution in variance decomposition highlights its role as an important source of external shocks. These findings are relevant to various stakeholders, including investors and policymakers. Additionally, policy emphasis should be placed on controlling inflation and maintaining stable interest rates while improving trade balance conditions. Although foreign capital flow does not show a direct long-run effect, its role in influencing market variability suggests the need for a stable and well-regulated investment environment. Full article

Predicting financial stock returns remains challenging due to their inherent nonlinearity, non-stationarity, and sensitivity to market microstructure noise. Existing approaches typically rely on either econometric filtering techniques or deep learning models in isolation, limiting their ability to jointly capture latent dynamics and complex temporal dependencies. This study proposes a hybrid Extended Kalman Filter–Long Short-Term Memory (EKF–LSTM) framework that integrates nonlinear state-space filtering with deep sequential learning. The EKF component performs nonlinear state estimation and denoises to extract latent signals from noisy observations, while the LSTM network models nonlinear temporal dependencies in the filtered series. The proposed framework is evaluated using data from multiple international markets, including China, the United States, and Europe, providing cross-market evidence of model robustness. Empirical results show that the EKF–LSTM model consistently outperforms benchmark models (ARIMA, standalone EKF, LSTM, and GRU) across standard statistical metrics, including RMSE, MAE, and mean directional accuracy (MDA). In addition, the model delivers economically meaningful improvements under a long-only trading strategy, achieving higher risk-adjusted returns and lower maximum drawdowns relative to benchmark strategies. Diebold–Mariano tests further confirm that these performance gains are statistically significant. Overall, the findings demonstrate that integrating nonlinear state-space filtering with deep learning provides a robust and effective framework for financial time-series forecasting. However, the results should be interpreted with caution due to the limited sample size and the simplifying assumptions underlying the trading strategy. Full article

Forecasting scrap steel prices is crucial for the economic sustainability of recycling operations, yet it remains challenging due to inherent volatility and non-stationary behavior. In this study, we develop and evaluate a delta-targeted Hybrid forecasting pipeline for short horizons of 1, 3, and 7 days. We benchmark classical baselines (Naive, Seasonal Autoregressive Integrated Moving Average (SARIMA), and Exponential Smoothing (ETS)) against recurrent deep learning models (Simple Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)) and recent neural forecasting baselines, including Decomposition-Linear (DLinear), Convolutional Kolmogorov–Arnold Network (C-KAN), and Neural Basis Expansion Analysis for Time Series (N-BEATS), using real-world daily scrap steel price data. The results indicate that delta-targeting generally yields more stable predictive performance than direct raw-price forecasting as the prediction horizon increases. For example, at the 7-day horizon, the predictive fit improves from approximately $R^2\approx 0.87$ for raw-price LSTM to around $R^2\approx 0.90$ for delta-trained recurrent models. At the same horizon, a delta-based RNN achieves the lowest Mean Absolute Percentage Error (MAPE) among the evaluated models (approximately $1.39\%$), while the proposed Hybrid model remains competitive across all tested horizons and maintains a goodness-of-fit of approximately $R^2\approx 0.90$ without uniformly minimizing point error relative to the best-performing recurrent baseline. Attention profiling and permutation-based feature importance analyses indicate that the model places relatively higher weight on calendar-related inputs, consistent with the presence of weekly patterns in the data; these results should be interpreted as sensitivity diagnostics rather than causal evidence. Overall, the findings suggest that delta-transformed targets provide a more suitable prediction space than raw-price targets for short-horizon scrap steel forecasting, while the Hybrid design offers a balanced combination of predictive performance and diagnostic interpretability for operational decision support. Full article

The integration of large-scale electric vehicle (EV) fleets into vehicle-to-grid (V2G) systems offers significant potential for enhancing the operation of renewable-based smart grids. However, stochastic uncertainties in photovoltaic (PV) generation, vehicle availability, and load demand—coupled with battery degradation and life-cycle assessment (LCA) carbon emissions—pose major challenges to optimal scheduling. This paper proposes a scenario-based multi-objective MILP framework for a 500-EV fleet aggregator. The model incorporates Monte Carlo simulations for multi-source uncertainty quantification (±25% PV forecast errors, ±40% availability), LCA penalties (45 kgCO₂eq/kWh), and ancillary service revenues (25 USD/MW-h). Long-term state-of-health (SOH) projections, including a 1-year fade to 96.5%, are also integrated. Comparative analysis of V2X scenarios shows that the V2G Hybrid strategy reduces daily costs by 34.6% (from ~11,000 USD in the uncontrolled case to 7741 USD when reserve revenues are included), achieves over 50% peak shaving, and maintains voltage stability within 0.994–1.008 pu. The stochastic Pareto frontier identifies knee-point solutions that lower normalized expected costs to 134.61 while achieving 1–2% lower expected emissions compared to deterministic baselines. These results demonstrate a comprehensive framework, uncertainty-aware framework that balances economic viability, grid resilience, and environmental sustainability, offering actionable insights for fleet aggregators and policymakers working toward net-zero energy systems. Full article

Severe transients in nuclear power plants (NPPs) are strongly coupled and highly nonstationary, which makes reliable short-horizon multivariate forecasting difficult for conventional sequence models. To address this challenge, this study develops a hybrid LSTM-Transformer forecasting framework for severe nuclear accident time series and uses the Sparrow Search Algorithm (SSA) as a task-oriented joint hyperparameter optimization tool for nuclear accident forecasting. In this framework, the self-attention mechanism captures long-range temporal dependencies and cross-variable interactions, while the LSTM component strengthens the modeling of short-term dynamics and local temporal memory. SSA is employed as a task-oriented joint hyperparameter optimization tool to adapt key model settings, including the number of attention heads, encoder depth, model dimension, LSTM hidden units, and dropout rate, for severe nuclear accident forecasting. In addition, a regularized training strategy combining dropout and validation-based early stopping is adopted to alleviate overfitting and improve training stability. The main comparison results are reported as mean ± standard deviation over 20 independent runs with the same data split and different random seeds. Experiments on high-fidelity PCTran/APR1400 simulations covering LOCA, LACP, and SLBIC scenarios, together with a severity-shifted LOCA test, demonstrate strong and statistically stable predictive performance. Across the three representative accident scenarios, the proposed framework achieves mean R² values of 0.943 ± 0.009, 0.951 ± 0.007, and 0.946 ± 0.010, while maintaining about 30% lower mean nRMSE and nMAE than the strongest LSTM-Transformer baseline. A 2 × 2 ablation study shows that regularization mainly improves training efficiency, reducing the required epochs by a range of about 36–41%, whereas SSA primarily improves predictive accuracy through better hyperparameter selection. Their combination provides the best overall generalization. Cross-severity LOCA evaluation further confirms the robustness of the proposed model, yielding mean R² = 0.885 ± 0.017 and mean nRMSE = 0.100 ± 0.010. The model also achieves low inference latency (P50 = 7.6 ms per sample), indicating its computational potential for near-real-time multivariate forecasting in safety-critical transient monitoring. Full article

Achieving carbon neutrality, enhancing energy efficiency, securing energy supply, and accurately forecasting energy demand are among the most urgent global energy priorities. In this study, Turkey’s geothermal, wind, and solar electricity consumption was forecasted for the 2025–2030 period using five years of historical data through eight different regression-based models. The forecast models included ARIMA, Linear Regression, Polynomial Regression, Exponential Smoothing, Ridge, Lasso, SVR, and XGBoost. Forecast accuracy was validated using 2023–2024 data. A hybrid model, integrating the Lasso and Random Forest approaches via weighted averaging, was developed to enhance forecast robustness. Pearson correlation was applied to quantify the impact of key socioeconomic variables—such as population, GDP, and university graduates—on energy consumption patterns. Forecast comparisons revealed that Random Forest and XGBoost produced results closest to the Hybrid model, with deviation rates of 1.84–7.27% and 0.03–1.08%, respectively. In contrast, Polynomial Regression and Exponential Smoothing showed significant biases, with deviations reaching up to 61.58% and 54.48% in 2030. ARIMA remained relatively consistent but exhibited increasing deviation over time. The Exponential and Polynomial models consistently overestimated demand, while SVR underestimated it throughout the forecast horizon. Ridge Regression provided stable but systematically higher forecasts. The findings indicate that the hybrid model provides a balanced forecasting structure and mitigates the under- or overestimation tendencies observed in singular models. This research supports strategic, data-driven energy planning in alignment with long-term sustainability goals. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

To address the challenges of strong volatility, pronounced non-stationarity, and the inability of single models to simultaneously capture local dynamics and global dependencies in photovoltaic (PV) power series under complex weather conditions, this study proposes a short-term PV power forecasting framework that integrates weather-based clustering, signal decomposition, parameter optimization, and hybrid neural networks. First, a density-based clustering algorithm, namely Density-Based Spatial Clustering of Applications with Noise (DBSCAN), is employed to partition historical samples into distinct weather regimes, thereby mitigating the impact of heterogeneous meteorological conditions on model stability. Second, to handle the strong non-stationarity of PV power series, Variational Mode Decomposition (VMD) is introduced to decompose the original signal into multiple intrinsic components. The Crested Porcupine Optimizer (CPO) is further utilized to adaptively optimize key VMD parameters, including the number of modes and the penalty factor, thereby improving decomposition quality. Finally, a hybrid LSTM–Transformer forecasting model is constructed to jointly capture local temporal dynamics and long-range dependencies. The Newton–Raphson-Based Optimizer (NRBO) is employed to optimize critical hyperparameters, including the learning rate, regularization coefficient, and the number of hidden units, thereby enhancing model performance. The proposed method is validated using real-world data from a PV power station in Alice Springs, Australia. Experimental results demonstrate that, compared with the LSTM–Transformer baseline, the proposed model achieves reductions in RMSE of 0.086, 0.082, and 0.097 kW, and reductions in MAE of 0.062, 0.082, and 0.081 kW under clear-sky, cloudy, and rainy/snowy conditions, respectively. The corresponding R² values reach 0.993, 0.968, and 0.958. These results indicate that the proposed framework exhibits strong predictive performance across different weather scenarios and provides a reliable reference for short-term PV power forecasting and grid dispatching decisions. Full article

This study investigates the integration of categorical inverter alarm data into data-driven frameworks for photovoltaic (PV) system monitoring. While most existing approaches rely exclusively on continuous SCADA measurements, the potential of categorical operational data remains largely unexplored. In this work, categorical alarm signals are incorporated into power forecasting to enable anomaly detection. The proposed approach is evaluated on a large-scale real-world dataset comprising multiple PV plants and more than 100 inverters, representing over 1000 inverter-years of operation. The four most popular time series forecasting models, including Multi-Layer Perceptron, Long Short-Term Memory, Extreme Gradient Boosting, and Mamba, are used to estimate power output from continuous inputs, while categorical variables are integrated using one-hot encoding and entity embeddings. Anomaly detection is performed by analyzing residuals between predicted and measured power output. The results show that categorical alarm data contain relevant operational information and can be effectively incorporated into forecasting-based monitoring frameworks. However, their impact on predictive performance varies depending on the encoding strategy and model choice, highlighting important trade-offs between model complexity and feature representation. By providing a systematic evaluation of categorical data integration across a large, diverse dataset, this work addresses a gap in the literature and establishes a benchmark for future research on hybrid continuous–categorical approaches for PV inverter monitoring. Full article

This study investigates the effectiveness of Bayesian probabilistic methods for stock price forecasting on the Johannesburg Stock Exchange by implementing and comparing Gaussian process regression (GPR), Bayesian long short-term memory (Bayesian LSTM), and Bayesian neural networks (BNNs). Using daily open, high, low, close, and volume (OHLCV) data and engineered technical indicators for FirstRand and Discovery from January 2005 to June 2025 (5187 observations), models were trained and evaluated with the mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE). The GPR produced reliable, well-calibrated intervals in relatively stable regimes, but its performance degraded on the more volatile Discovery series. Bayesian LSTM delivered conservative uncertainty estimates with wide predictive intervals but showed the largest point forecast errors. The BNNs achieved the best balance between accuracy and uncertainty quantification, producing the lowest errors for FirstRand and competitive performance for Discovery. Comparative analysis indicates that BNNs are most suitable when point accuracy and calibrated uncertainty are both priorities, GPR is valuable for smaller or more stable data regimes, and Bayesian LSTM is preferable where conservative, risk-conscious intervals are required. This study highlights the practical value of embedding uncertainty into financial forecasts and recommends matching Bayesian model choice to market volatility, data availability, and decision maker risk appetite. Full article

Remaining Useful Life (RUL) forecasting models are essential to enable predictive maintenance strategies. However, selecting the most appropriate model based solely on conventional accuracy metrics may be insufficient for practical decision making, where an adequate prediction horizon is required to plan maintenance activities. This study investigates the impact of prediction horizon on model performance and its implications for maintenance decision making. A multi-horizon evaluation approach is applied to assess model accuracy across different predictive horizons. The results show the fluctuation of accuracy and prediction error over different prediction horizons. Across both datasets, predictive accuracy was generally lowest at the long horizon (11.64–86.62%), remained variable at the medium horizon (18.13–82.04%), and was highest at the short horizon (30.29–98.25%). The results demonstrate that model performance varies significantly with the prediction horizon, highlighting a trade-off between prediction accuracy and the time available for operational planning. These findings emphasize that models with high short-term accuracy may not necessarily support effective maintenance decisions if sufficient lead time is not provided. The findings show how prediction horizon considerations shall be integrated into a risk-based evaluation framework, in which model performance is interpreted in relation to the operational consequences of prediction errors. A complete risk-based predictive maintenance framework is proposed to support a shift toward comprehensive, risk-based evaluation as a prerequisite for reliable and effective RUL prediction in predictive maintenance systems. Full article

Monkeypox (Mpox), a zoonotic Orthopoxvirus disease, was declared a Public Health Emergency of International Concern by the WHO in 2022. While the global fatality rate is approximately 0.7%, mortality risks vary significantly across regions and remain high for vulnerable groups, necessitating precise predictive models for public health resource allocation. This study proposes TRAGIC, a novel fusion model integrating Transformer, GRU, and Quick Attention mechanisms to predict monkeypox death cases. Utilizing a global dataset with a comprehensive set of 16 input features, the TRAGIC model was benchmarked against traditional GRU, LSTM, Transformer, and Trans-GRU architectures. Experimental results demonstrate that TRAGIC consistently outperforms existing deep learning models, particularly in capturing non-linear patterns and long-term dependencies in epidemic time-series data. The findings suggest that the TRAGIC model offers superior accuracy and stability, providing a robust tool for forecasting infectious disease mortality and supporting global health policymaking. Full article

Air quality affects large urban areas, where rapid urban development and human activities place constant pressure on ecosystems and public health. In this context, large-scale air quality assessment, supported by short-term forecasts, can provide useful information for environmental management and decision-making in urban areas, thus supporting evidence-based urban environmental management. The aim of this work is to design an affordable, smart real-time air pollution monitoring and prediction system for urban planning in overpopulated locations, which is deeply related to community health. The system focuses on real-time monitoring and forecasting of air quality. Prediction tasks were limited to gaseous pollutants CO and CO₂. Measurements were obtained over four months from a low-cost sensor platform installed in a highly populated neighborhood district in Baghdad, Iraq. Air quality prediction of gas concentrations was done using three types of time-series algorithms: Long Short-Term Memory, or LSTM; Gated Recurrent Unit, or GRU; and Temporal Convolutional Network, or TCN, models. Among these, the LSTM architecture showed more stable behavior and a higher predictive ${\mathrm{R}}^2$, ranging from 98.2% to 98.9%. Generally, the findings suggest that combining low-cost sensing technologies with artificial intelligence can offer a feasible and scalable solution for urban air quality monitoring. This approach may support cost-effective strategies for monitoring air quality in resource-constrained urban environments. Full article

Accurate long-term forecasting of Quality of Transmission (QoT) is critical for the proactive operation and condition-aware management of dynamic elastic optical networks. However, the evolution of QoT is governed by multi-scale dynamics, including slow equipment aging, periodic operating variations, and short-term channel fluctuations, which a single temporal model struggles to capture jointly. To address this issue, we propose PA-TCN-Informer, a decomposition-driven parallel forecasting framework for long-horizon QoT prediction. The proposed framework first applies Seasonal-Trend decomposition using Loess (STL) to separate the Q-factor sequence into trend, seasonal, and residual components, and then employs Variational Mode Decomposition (VMD) to further resolve the residual into short-term fluctuation modes. The decomposed components, together with physical-layer monitoring features, are fed into a parallel TCN–Informer architecture, in which the TCN branch captures local temporal patterns while the Informer branch models long-range dependencies; the two streams are subsequently fused. We evaluate the proposed framework through Optuna-based hyperparameter optimization, STL/VMD sensitivity analysis, decomposition-method comparison, multi-seed baseline comparison with statistical testing, and zero-shot leave-one-dataset-out cross-domain evaluation. On the primary dataset, PA-TCN-Informer achieves the best overall forecasting accuracy among the compared models and reduces MAE by 2.2% relative to the serial TCN–Informer. In addition, the staged STL-VMD preprocessing alone yields a 60.8% reduction in MAE compared with raw inputs, confirming the value of physically interpretable multi-scale decomposition. In the zero-shot cross-domain setting, PA-TCN-Informer remains competitive across target domains. These results demonstrate that the proposed framework provides an effective and interpretable approach to QoT forecasting, and they further indicate that topology-aware modeling is a promising direction for improving cross-domain generalization. Full article