Search Results (1,026)

This work evaluates a hybrid harmonic and data-driven approach for short-term sea-level forecasting in the Venice lagoon. The observed sea level is decomposed into an astronomical component, estimated through harmonic analysis, and a non-astronomical component, modelled using autoregressive (ARX) and neural network autoregressive (NNARX) methods with (and without) exogenous covariates. The forecasting experiment uses a rolling-origin out-of-sample design over 120 hourly horizons, with 2023 as the test period. The results show that the harmonic model explains approximately 66% of the total variance, providing a stable astronomical benchmark. Data-driven models substantially improve upon this benchmark, especially when wind components, atmospheric pressure, and the MoSE activation records are included as covariates. ARX and NNARX achieve the highest explained variance and the lowest forecast errors across most horizons. However, they are statistically similar over the full sample, while NNARX provides significant gains over several medium and long horizons under high-water conditions. Full article

Stock market volatility forecasting is important for risk management, portfolio allocation, and investment decision-making. This study provides a bibliometric and methodological review of stock market volatility measurement and forecasting research published between 2016 and 2025. A model-neutral search of the Web of Science Core Collection, conducted on 18 August 2026, identified 440 records. Following title, abstract, full-text, and document-type screening, 177 eligible journal articles were retained. To assess search-term sensitivity, a supplementary search conducted on 4 September 2026 using alternative volatility terminology identified 33 additional eligible studies, yielding a final corpus of 210 studies. Bibliometrix/Biblioshiny and structured methodological classification were used to examine the field. Econometric approaches remained dominant (168 studies; 80.0%), followed by Machine Learning (25; 11.9%), Deep Learning (8; 3.8%), and Hybrid approaches (9; 4.3%). The evidence reveals substantial methodological diversification beyond conventional GARCH models and increasing use of realized and implied volatility, high-frequency information, sentiment, macroeconomic variables, and uncertainty indicators. No methodological family demonstrates universal forecasting superiority, as performance depends on markets, horizons, information sets, benchmarks, and evaluation criteria. Overall, the literature reflects methodological diversification, information enrichment, and selective integration rather than replacement of econometric models by artificial intelligence. Although the review is limited to the Web of Science Core Collection, the sensitivity analysis demonstrates the importance of alternative terminology in identifying relevant studies. Full article

Forecasting equity returns remains challenging because financial markets exhibit nonlinear dynamics, volatility clustering, and complex temporal dependencies that are difficult to capture using a single modelling approach. Traditional statistical models can capture dependence and volatility dynamics, while deep learning models are capable of learning nonlinear temporal patterns. This study evaluates the forecasting performance of traditional statistical models (ARIMA and GARCH), deep learning models (TCN and GRU), and hybrid models (ARIMA-TCN, ARIMA-GRU, GARCH-TCN, and GARCH-GRU) for forecasting the FTSE/JSE Top 40 index in South Africa. Daily closing prices comprising 4159 observations from 2010 to 2026 were analysed, with model performance evaluated on log returns using MSE, RMSE, and MAE, with the Naïve model used as a benchmark. The results show that all competing models substantially outperformed the Naïve benchmark, while the TCN outperformed the standalone GRU. GARCH-based models also demonstrated strong forecasting performance, highlighting the relevance of volatility dynamics in forecasting equity returns. Although GARCH-GRU recorded the lowest MSE, RMSE, and MAE among the models evaluated, its performance was very similar to that of the GARCH (2,2) model. The Diebold-Mariano test further showed no statistically significant difference in predictive accuracy between GARCH-GRU and $GARCH (2,2)$ (DM = 0.9495; p = 0.3424). These findings indicate that GARCH-GRU and $GARCH (2,2)$ provide statistically comparable forecasting performance, suggesting that the additional complexity of the hybrid framework does not necessarily result in a significant improvement in predictive accuracy. This study contributes to the financial forecasting literature by providing empirical evidence from an emerging African equity market and demonstrating the importance of volatility modelling and complementary statistical–deep learning approaches for forecasting FTSE/JSE Top 40 log returns. Full article

Accurate forecasting of electrical load at the substation level is essential for detecting grid losses that pose significant financial and safety challenges. This study investigates whether the standard correction method currently used by grid operators can be improved through the application of advanced forecasting models to granular, location-specific load data. Such improvements are particularly valuable for identifying abnormal consumption patterns indicative of grid losses due to electricity theft, defective meters, or cable damage. This paper evaluates a broad range of statistical and machine learning models—including ARIMAX, SARIMAX, Random Forests, Gradient Boosting Machines, Neural Networks, and Support Vector Regression—based on unique quarter-hourly datasets from several Dutch substations. Two hybrid approaches are proposed, combining the best-performing individual models through a stacked ensemble method and a simpler averaging strategy. The results show that incorporating lagged and additional exogenous variables, along with the application of various advanced models, significantly improves forecasting accuracy compared to the standard correction method, with the best hybrid model reducing MAE and RMSE by approximately 64.7% and 61.6%, respectively, relative to the current operational benchmark. This study demonstrates that substation-level, data-driven forecasting can strengthen the signals used to detect grid losses, offering practical implications for grid operators and policymakers. Full article

Accurate modelling of price formation in electricity markets requires capturing the underlying supply and demand structure rather than predicting scalar prices alone. This structure is based on the participants’ bidding process, who submit energy volume offers in the form of block orders and hybrid curve orders. This work addresses the problem of day-ahead forecasting of aggregated hybrid supply curves in the Greek DAM at a 15 min resolution. A hybrid methodology is proposed that combines FPCA, which reduces the high-dimensional curve space to a small set of latent factors, with gradient-boosted forecasts that recursively use models driven by calendar effects, lagged latent dynamics, and exogenous variables (load, RES, and cross-border capacities). Forecasted curves are reconstructed and refined via band-specific residual correction and feasibility constraints. A baseline FPCA backed by ARIMA residual forecasting is used for comparison. The approach is validated for January–March 2026 using a strict day-ahead setup. Evaluation over selected days between January and March 2026 demonstrates that the proposed methodology generally improves forecasting accuracy and maintains stronger performance under evolving market conditions. Results demonstrate that the proposed framework effectively captures curve dynamics, while emphasising the importance of system-state information under changing conditions. Full article

Predictive analytics has emerged as an important component of hospital automation by enabling proactive resource management and data-driven decision-making. Efficient allocation of patient monitors in operating rooms represents a practical application where forecasting can support perioperative safety and resource management. This study compared five forecasting models for predicting patient monitor availability using integrated clinical, operational, and equipment management data to identify appropriate forecasting approaches for operating room resource planning. We conducted a retrospective longitudinal study using monthly surgical operational data from the anesthesia information system and equipment-related data from the medical equipment management system of a tertiary referral hospital. The outcome was the monthly number of available patient monitors recorded in the equipment management system, which served as the reference value for evaluating five prediction models, including naïve persistence, autoregressive integrated moving average (ARIMA), multivariable linear regression, LSTM, and hybrid LSTM–regression. Model performance was evaluated using root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Across a 36-month study period, the mean monthly surgical volume was 1960 (SD 182) procedures, with a mean operative duration of 105.0 (SD 3.5) minutes. Weighted multivariable regression showed that service age (standardized β = 0.72, 95% CI 0.66–0.78), maintenance frequency (standardized β = 0.09, 95% CI 0.03–0.15), and surgical volume (standardized β = 0.15, 95% CI 0.09–0.21) were positively associated with available patient monitor, whereas mean operative duration was inversely associated (standardized β = −0.19, 95% CI −0.26 to −0.12). The naïve persistence (RMSE 0.17, MAE 0.03, MAPE 0.16%) and ARIMA (RMSE 0.17, MAE 0.04, MAPE 0.21%) showed higher predictive performance, while the multivariable linear regression, LSTM-only, and LSTM–regression models showed relatively higher prediction errors. Beyond predictive accuracy, the model provides an operational framework for transforming routinely collected clinical and equipment data into actionable information for automated resource planning. This study showed that predictive analytics can support hospital automation by enabling proactive patient monitoring and resource planning. Classical statistical models remain robust alternatives for hospital resource forecasting in small-sample settings, while regression-based approaches provide interpretability for operational decision-making. Full article

Sustainable water resource management in semi-arid Mediterranean regions is critically dependent on reliable spring discharge forecasting and on quantifying the long-term impacts of climate change on groundwater resources. This study develops and evaluates a suite of non-autoregressive machine learning architectures, MLP, CNN, LSTM, and a hybrid CNN–LSTM, for predicting the monthly discharge of major springs supplying Palermo, Italy. Unlike autoregressive approaches, which are susceptible to error accumulation in multi-step projections and cannot be applied under projected climate conditions, the proposed models rely exclusively on exogenous meteorological inputs, making them inherently suited for operational forecasting and architecturally compatible with long-term climate impact assessment. The CNN–LSTM architecture achieves the best performance among all non-autoregressive configurations, approaching the idealized autoregressive benchmark while avoiding its operational limitations. In the operational phase, the calibrated CNN–LSTM model is driven by bias-corrected ECMWF SEAS5 seasonal forecasts, maintaining useful predictive performance up to six months ahead for RIS, SCI, and the aggregated system (SYS), with more limited skill for GAB at longer lead times. Diebold–Mariano tests confirm that this advantage is statistically significant for two of the three springs, whereas for the third, whose dynamics are dominated by internal aquifer memory, no non-autoregressive architecture prevails. This framework not only enables retrospective analysis of spring discharge behavior but is also, thanks to its non-autoregressive design, structurally suited for future coupling with climate projections, offering water authorities an effective solution for proactive and sustainable management of this crucial Mediterranean water resource. Full article

Accurate forecasting of CO₂ emissions is essential for evaluating emission-reduction pathways and supporting progress toward carbon-peaking and sustainable-development goals. Such forecasting is often challenging because macroeconomic datasets are small and exhibit strong nonlinear relationships. This study develops SA-GA-BP, a hybrid forecasting framework that integrates sensitivity analysis, Genetic Algorithm, and a Back Propagation neural network. A leave-one-variable-out sensitivity analysis uses changes in model performance to identify seven variables with high predictive importance and reduce input redundancy. GA then optimizes the BP network’s initial parameters to reduce the risk of local optima during small-sample training. Under the train–test split used in this study, the SA-GA-BP model yielded an R² of 0.98766 and an RMSE of 2.6598, showing favorable numerical prediction performance among the compared models. In the low-carbon scenario, emissions peak in 2028 at 12.18 Gt CO₂; under the high-carbon scenario, no peak occurs by 2030. The results suggest that accelerated energy-efficiency improvement, reduced fossil-fuel dependence, and continued industrial restructuring are important for achieving an earlier and lower carbon peak. The framework provides a practical approach to small-sample emission forecasting and supports scenario-based assessment of China’s carbon-peak pathway and sustainable development. Full article

In this work, Intelligent Bidirectional Long Short-Term Memory with Temporal Fusion Transformer-based prediction and Proximal Policy Optimization (IBT-PPO) is proposed in response to the challenges of uncertain renewable generation, fluctuating demand, and inefficient energy scheduling in hybrid renewable energy systems. The algorithm is based on a dual-stage framework that integrates machine learning forecasting with reinforcement learning-based planning. Initially, a hybrid Bi-LSTM-TFT model is employed to generate accurate short-term forecasts of wind power, solar power, and demand, which employs temporal dependencies and multi-horizon patterns. After that, the PPO strategy is designed to optimize scheduling decisions, adaptively balancing battery usage, grid reliance, and renewable dispatch. To enhance robustness, adaptive feature weighting and temporal gating strategies are incorporated, ensuring stable convergence and reduced planning redundancy. Subsequently, the energy allocation is refined through iterative learning to minimize operational cost and maximize renewable penetration. The proposed framework is evaluated as an offline/post hoc forecasting and scheduling approach, with the Bi-LSTM–TFT module exploiting historical temporal representations and the PPO agent optimizing energy-management decisions based on the resulting forecasts. The experimental evaluation is carried out using the Open Power System Data (OPSD) dataset, which provides realistic time-series data for wind, solar, demand, and electricity prices. Thus, the IBT-PPO system integrates multi-horizon probabilistic forecasting and adaptive feature weighting for better prediction and planning accuracy and achieves a 24.1% cost reduction and 95.5% renewable utilization, thereby advancing efficient and intelligent energy prediction and planning. Full article

Accurate solar irradiance forecasting in data-sparse tropical regions remains challenging due to complex terrain, rapid convective cloud formation, and limited ground-based observations. This study introduces a novel hybrid forecasting framework that integrates the Weather Research and Forecasting (WRF) model (10 km) with station-based Long Short-Term Memory (LSTM) bias correction, complemented by three innovative spatial site-adaptation strategies to produce spatially coherent short-term irradiance fields. The hybrid system leverages hourly Global Horizontal Irradiance (GHI) data from eight BMKG stations (2023) alongside GK2A satellite cloud information to dynamically correct WRF forecast biases, capturing nonlinear cloud–irradiance interactions that standard Numerical Weather Prediction (NWP) models fail to resolve. Results indicate that the hybrid WRF–LSTM system reduces 1–3-day root mean square error (RMSE) by 120–127 W/m² and relative RMSE (rRMSE) by 26%, while lowering relative mean bias error (rMBE) from 31–36% (raw WRF) to 2.5–4.1%, with the largest improvements observed in regions exhibiting initially high WRF errors. Among the spatial adaptation methods, the average-based scheme minimizes RMSE but exhibits weak spatial coherence; the distance-weighted scheme achieves the strongest spatial consistency with regional reanalysis (R² = 0.37) with minimal bias; and the elevation-based scheme ensures full-domain coverage with moderate skill. This study demonstrates that the integration of dynamical NWP modeling with LSTM-based bias correction and tailored spatial transfer strategies provides a robust, scalable approach for short-term solar irradiance forecasting and resource mapping in tropical environments. The proposed framework offers practical implications for PV power forecasting, grid management, and renewable energy planning in regions where observational data are sparse and the terrain is highly heterogeneous. Full article

The building sector accounts for 34% of global energy demand, making accurate short-term load forecasting essential. Small commercial buildings remain challenging due to high-frequency noise and data sparsity. This study proposes a hybrid machine learning framework that decouples behavioral load shapes from climate-driven magnitude variations. K-Means clustering identifies operational archetypes, while a dual-stage supervised pipeline predicts daily shapes via a non-linear SVM classifier and peak magnitude via regression. Validated on real-world 15 min office building data from Murcia, Spain, the framework consistently outperforms ARIMAX, Prophet, persistence, and zero-shot foundation models (TimesFM, Chronos-t5). The best configuration achieved R² = 0.83 and MAE = 0.37 kW, confirming that domain-specific behavioral decoupling surpasses general-purpose pretraining in small-scale commercial datasets. Full article

Accurate short-term traffic forecasting and efficient dynamic route guidance are pivotal for mitigating urban congestion. However, accurately capturing complex temporal dependencies and nonlinear variations in traffic flow remains challenging, while conventional path-planning algorithms may also suffer from local-optimum problems. To address these challenges, this study proposes a hybrid framework integrating an optimized prediction model with an enhanced ant colony optimization algorithm. First, a GA-BiGRU-ATT model is developed for traffic state prediction. By combining a bidirectional gated recurrent unit (BiGRU), a temporal attention mechanism, and genetic algorithm-based hyperparameter optimization, the model captures contextual temporal dependencies within the observed historical input window and emphasizes critical time-step features. Under the evaluated model configurations, the proposed approach achieved traffic-state classification accuracies of 90.28% and 87.50% on Segments 1 and 2, respectively. Second, based on the predicted traffic states, an Improved Ant Colony Algorithm (IACA) incorporating traffic-state feedback is proposed to mitigate slow convergence and local-optimum entrapment in conventional ACO. Within the ant-colony-based comparison, the IACA reduced the average computational time by 49.57% relative to the conventional ACA. Furthermore, under the evaluated SUMO evening-peak scenario, periodic dynamic guidance reduced the average travel time of the selected guided vehicles by 12.06% and increased their average speed by 27.07%. These results demonstrate the potential of prediction-guided dynamic rerouting under the evaluated simulation conditions. Full article

With the advancement of the new-type power system, the exponentially growing high-frequency distribution and consumption data imposes heavy transmission and processing pressure on resource-constrained edge devices. Existing compression methods face two core limitations: static algorithm configurations that fail to adapt to dynamic time-varying power load characteristics, and complex computations that are difficult to deploy on resource-constrained edge terminals. To address these issues, this paper proposes a cloud-edge collaborative adaptive compression method based on CNN-LSTM load forecasting and Q-learning decision-making. A three-layer “cloud-edge-terminal” architecture is built to decouple compression decision-making from edge execution. The cloud employs a hybrid one-dimensional CNN and single-layer LSTM (1D-CNN-LSTM) for high-precision short-term load forecasting, and establishes an adaptive Q-learning decision mechanism to issue differentiated compression instructions according to varying load characteristics. The edge terminals receive these instructions and perform lightweight lossless compression accordingly. Simulation results show that the CNN-LSTM model achieves a MAPE of 7.59%. The Q-learning agent converges to an average reward of 65.35% during training and achieves a 66.73% overall compression ratio on the unseen test set, outperforming the fixed LZW baseline by approximately 6 percentage points. Furthermore, the proposed method improves the edge processing throughput by approximately 6.5 to 10.4 times compared to the comparative baselines. These results suggest that the cloud-edge collaborative approach offers a promising direction for alleviating edge pressure and balancing compression efficiency with computational overhead in massive power data transmission scenarios. Full article

The power demand of High-Performance Computing (HPC) infrastructures exhibits both stable weekly regularities and rapid workload-driven fluctuations, which are difficult to capture reliably with a single modeling paradigm. Achieving more sustainable HPC operation requires accurate forecasts at multiple horizons: short-term predictions support operational control (e.g., proactive power capping and energy-aware scheduling), whereas long-term forecasts are essential for planning activities (e.g., capacity provisioning and energy procurement). Together, these capabilities reduce operational cost and risk while enabling more efficient and sustainable datacenter management. This paper investigates multi-horizon forecasting of aggregated active power consumption in an operational HPC datacenter utilizing a four-month dataset (five-minute time intervals) from the University of Turin. We propose a hybrid residual-learning framework that integrates a long-term structural forecaster with a short-term residual corrector utilizing a Temporal Convolutional Network (TCN) to address the simultaneous presence of weekly regularities and short-term workload-induced fluctuations. Assessment utilizing a rolling-origin protocol covers a timeframe of 15 min to 6 h and extends 1 to 3 weeks into the future. Performance of the proposed approach has been compared against the SARIMA baseline. Full article

This study presents a leakage-proof hybrid machine learning framework for early flood risk forecasting using multimodal spatiotemporal tabular data. An event-based dataset covering Kazakhstan from 2001 to 2021 was created by integrating topographic characteristics, hydrometeorological variables, long-term surface water dynamics, and remotely sensed spectral indices. To ensure realistic assessment, only pre-event observations were used, and event-based temporal data separation was employed to prevent leakage between the training and test subsets. The proposed Remote Sensing Adaptive Linear Opinion Pool Machine Learning (RS-ALOP-ML) framework combines multiple logistic regression experts with different regularization strengths through an Adaptive Linear Opinion Pool (ALOP) probabilistic fusion strategy, thereby preserving interpretability while improving forecasting robustness. The proposed framework was evaluated for four independent forecast horizons (T + 1, T + 7, T + 14, and T + 30 days) and compared with traditional machine learning algorithms and state-of-the-art tabular deep learning models, including Random Forest, ExtraTrees, XGBoost, LightGBM, CatBoost, MLP, FT-Transformer, TabNet, and Process Wide&Deep. Experimental results demonstrate that the proposed hybrid approach consistently achieves competitive or superior forecasting performance while maintaining computational efficiency and transparent probabilistic outputs. The study highlights that carefully designed hybrid machine learning architectures, combined with leakage-safe evaluation protocols, provide a robust foundation for multimodal environmental forecasting and decision support applications. Full article