Forecasting

The study documents the impact of the external sector on movements of the Pakistan Stock Exchange (PSX), covering conventional and Islamic indices. Selected variables include international trade, foreign investment, remittances, oil, gold, and currency markets, as well as the KSE-100 and KMI-30 indices. The sample period covers the latest 130 months, from 2015/01 to 2025/10. Results are documented through descriptive statistics, pairwise correlations, and OLS regression. Stability of coefficients during the review period is checked by calculating BTC-Var and switching Var. Outstanding momentum is evident in market indices (in the final phase), accompanied by growth in remittances, while the national currency has experienced an alarming depreciation. The combined impact of the external sector is not in the higher range for either index (adjusted R-square values are low). A group of four variables (remittances, oil, gold, and currency markets) was significant for the conventional index, while a group of three variables (oil, gold, and currency markets) was significant for the Islamic index. All significant variables contribute positively to stock index movements, except the exchange rate. BTC-Var and switching var suggest instability of relationships and regime-dependent var dynamics. The findings are beneficial for managers and investors in predicting index movements and portfolio diversification, as well as for relevant authorities in making policy decisions that promote prudent exchange-rate management and facilitate remittances. To the best of the author’s knowledge, this study is among the few that jointly examine the impact of external-sector variables on stock market movements. Full article

This study investigates regime-dependent forecasting of the Saudi stock market by combining macro-controlled dependence analysis with nonlinear predictive modeling. Using daily data from September 2010 to August 2025, we analyze the interaction between the Tadawul All Share Index (TASI) returns and crude oil returns while controlling for inflation and interest-rate dynamics. A four-variable VAR with macro controls is estimated separately in pre- and post-COVID regimes to characterize directional predictability and changes in transmission lags. We then evaluate out-of-sample return forecasting performance across econometric benchmarks (ARIMA, ARIMAX, and VAR) and machine learning models (LSTM and XGBoost) under a strictly time-ordered expanding-window design with sequential train/validation/test partitioning. The results indicate that traditional linear benchmarks exhibit limited predictive ability in both regimes, with negative out-of-sample explanatory power. By contrast, XGBoost delivers the strongest overall performance, achieving positive out-of-sample R² in both regimes (0.046 in pre-COVID and 0.010 in post-COVID), together with the lowest forecast errors (RMSE = 0.0081 pre-COVID; 0.0078 post-COVID). Interpretability analysis further reveals a regime-sensitive shift in drivers: short-horizon equity lag dynamics dominate during stable periods, whereas oil-related and macro-financial variables gain importance under turbulent conditions. Economic-value evaluation supports the practical relevance of these gains, showing that XGBoost-based signals yield superior risk-adjusted trading outcomes and remain favorable under downside-risk and drawdown-based assessment. Overall, these findings highlight that forecasting in oil-linked emerging markets is inherently regime-dependent and that nonlinear ensemble learners, particularly XGBoost, provide a more robust and economically meaningful approach under structural change. Full article

Persistent fiscal stress in South African municipalities undermines service delivery, yet practical tools for early detection remain limited. This study predicts one-year-ahead municipal financial distress to support risk-based prioritisation. We develop machine learning models using a 2018/19–2022/23 municipality panel, combining 13 financial health indicators from State of Local Government (SoLG) reports with selected socio-economic variables. Penalised logistic regression is benchmarked against random forest and XGBoost under a leakage-aware, time-ordered split into training, validation, and an out-of-time test year; class imbalance is handled through class weighting. Performance is evaluated using PR-AUC, ROC-AUC, calibration, and a capacity-constrained Top-30 rule. All models outperform a naïve last-year baseline on the out-of-time test (PR-AUC 0.934–0.954; ROC-AUC 0.886–0.923), with bootstrap intervals supporting robustness. Random forest performs best overall, while penalised logistic regression remains competitive. Under the Top-30 rule (12.3% workload), precision is high (precision@30 0.967–1.000) while recall is modest (recall@30 0.186–0.192). SHAP values and logistic odds ratios identify liquidity, solvency, cash coverage, and employment deprivation as key drivers. The Top-30 rule corresponds to an annual intensive monitoring portfolio that is reasonable under constrained staffing and budget capacity in national and provincial oversight units, while probability thresholds are reported as conventional benchmarks rather than as policy triggers. Full article

The SAIFIN project (Satellite data and Artificial Intelligence for FINtech) develops a novel algorithmic trading system that integrates satellite imagery, financial data, and advanced artificial intelligence to enhance decision-making, particularly in commodity and agricultural markets. This paper presents the motivation, design, implementation, and validation of the SAIFIN framework. Leveraging alternative data and modular multi-agent architectures, SAIFIN aims to deliver robust, context-aware trading signals in diverse market conditions. Full article

New Zealand, located along the boundary between the Pacific and Australian plates, is among the most seismically active regions in the world. In such an area, reliable short-term forecasting of strong aftershocks is essential for seismic risk mitigation. In this study, we apply NESTORE (NExt STrOng Related Earthquake), a machine learning probabilistic forecasting algorithm, to the New Zealand earthquake catalogue to evaluate the probability that a mainshock of magnitude M_m will be followed by an event of magnitude ≥ M_m − 1 within a defined space–time window. NESTORE uses nine features describing early post-mainshock seismicity and outputs the probability that a cluster is Type A (i.e., containing a strong aftershock) or not (Type B). We assess performance using two testing strategies: chronological training–testing splits and k-fold cross-validation and refine the training set using the REPENESE outlier-detection procedure. The k-fold approach proves more robust than the chronological one, despite changes in catalogue characteristics over time. Eighteen hours after the mainshock, NESTORE correctly classified 88% of clusters (75% for Type A and 92% for Type B; Precision = 0.75). Notably, the highly destructive 2010–2011 Canterbury–Christchurch sequence was correctly identified as Type A. These findings support the applicability of NESTORE for short-term aftershock forecasting in New Zealand. Full article

Accurate wind power forecasting is critical for enhancing the operational efficiency and stability of electrical power grids. Conventional single-variable signal decomposition forecasting methods ignore the coupling relationship between wind power and multiple meteorological data, thus limiting prediction accuracy. This study proposes an accurate and fast short-term wind power prediction approach based on series-core fusion technology considering multiple meteorological data. In the data preprocessing stage, the multivariate variational mode decomposition (MVMD) algorithm decomposes wind power and meteorological variables into the same predefined number of frequency-aligned intrinsic mode functions (IMFs), thereby enhancing feature representation and improving forecasting accuracy via a more comprehensive and detailed dataset representation. During the training stage, the series-core fused time series (SOFTS) model establishes the connection among wind power channel and other meteorological variable channels for each IMF, achieving fast convergence through its streamlined and parallel structure. In the forecasting stage, the final wind power prediction is generated by the reconstruction of all IMFs. Furthermore, we conducted a comprehensive performance evaluation by comparing the proposed MVMD-SOFTS model with eight alternative models, including the CNN model, the TCN model, the LSTM model, the GRU model, the Transformer model, the SOFTS model, the CEEMDAN-SOFTS model, and the VMD-SOFTS model. The results indicate that MVMD-SOFTS outperformed all other models, demonstrating its effectiveness in capturing the multifaceted relationships in wind power forecasting. Full article

Sustainable corporate governance plays an essential role in promoting responsible economic growth and enhancing social and environmental well-being in emerging economies. In this context, Environmental, Social, and Governance (ESG) performance has become an important indicator of a firm’s commitment to sustainable development and its alignment with the United Nations Sustainable Development Goals, particularly SDG 8 and SDG 12. This study investigates the impact of Environmental, Social, and Governance (ESG) performance on the financial sustainability of publicly listed companies in Thailand, a rapidly developing Southeast Asian economy where empirical evidence remains limited. Using an unbalanced panel dataset of 965 firm-year observations across multiple industries, multiple regression models were employed to assess the influence of ESG performance on two financial indicators: return on capital employed and return on assets. Granger causality tests were also conducted to explore directional relationships between sustainability performance and financial outcomes. The empirical results reveal a significant negative short-term association between ESG performance and return on assets (ROA), whereas the relationship with return on capital employed (ROCE) is statistically insignificant. The causality analysis indicates that ESG performance Granger-causes ROA, implying that sustainability-driven strategic decisions may precede and influence financial outcomes over time. Additionally, leverage emerges as a key constraint to financial sustainability, negatively affecting both ROCE and ROA. These findings underscore the challenge of striking a balance between sustainability investments and immediate profitability in emerging markets. Policymakers and business leaders are encouraged to promote supportive governance frameworks, reduce financial barriers, and foster ESG-driven practices that contribute to long-term sustainable competitiveness and inclusive development. Full article

The main objective of this work is to characterize Sudden Stratospheric Warming (SSW) conditions and their impact on local weather forecasting and climate change, using SSW definition criteria. The SSWs strongly affect Arctic vortex structure and midlatitude weather conditions. This work evaluates the frequency, amplitude, and dynamical–thermal characteristics of SSWs under historical and Representative Concentration Pathway (RCP) 4.5 scenarios, focusing on stratospheric air temperature (T_s) and zonal wind speed (U_h) at the 10° N and 60° N latitudes. The fifth-generation ECMWF atmospheric reanalysis (ERA5) is employed as the reference dataset. Simulations of five Coupled Model Intercomparison Project Phase 5 (CMIP5) models, represented by M1 to M5, are analyzed. The primary group of models included 1) the Australian Community Climate and Earth-System Simulator, version 1.3 (ACCESS1-3, M1), 2) the Hadley Center Global Environmental Model, version 2—Carbon Cycle (HadGEM2-CC, M2), and 3) the Max Planck Institute Earth System Model—Medium Resolution (MPI-ESM-MR, M3). The analysis period covers SSW events related to the Quasi-Biennial Oscillation (QBO) in the Northern Hemisphere (NH) from 1980 to 2100. The key findings indicate that while M1, M2, and M3 simulate SSW occurrence correctly for the 21st century, they exhibit significant systematic deficiencies in capturing the structural dynamics of SSW events. Specifically, the M1, M2, and M3 models underestimate the polar stratospheric temperature amplitude (T_amp) by approximately 75–80% and zonal wind amplitude (U_amp) by more than 60% compared to the ERA5 analysis. Furthermore, ERA5 exhibits a strong negative correlation (R ≈ −0.8) between U_h and T_s that is not estimated accurately using the present models. The importance of the horizontal resolution of the models and wave–mean flow interactions in determining SSW intensity and occurrence is also found to be a critical metric. Results suggest that SSW definition criteria affect Arctic and midlatitude weather system prediction at a rate of 61–82%. It is concluded that the primary configurations of CMIP5 models for accurately capturing the dynamical structure and evolution of QBO–SSW interactions are needed, and that they affect future projections of SSW events. Full article

Temperature is a fundamental property of water that determines its quality and the course of both biotic and physical processes. Therefore, the distribution and future changes in thermal conditions are crucial for the functioning of the hydrosphere. In this study, a hybrid air2water model was used to determine the course of the sea surface temperature, which allows for its prediction using a minimal set of input data based on the air temperature. The widespread availability of air temperature measurements worldwide offers broad potential for the model’s application, which is especially important in coastal zones—the most dynamic and diverse areas of marine ecosystems, and simultaneously the most exposed to anthropogenic pressure. The study analyzes four hydrological stations in the southern part of the Baltic Sea, where the results confirm the high predictive capabilities of the air2water model for sea surface temperature. Depending on the adopted climate change scenarios, the average rate of sea surface temperature increase by the end of the 21st century is projected to be 0.15 °C per decade (SSP2-4.5) and 0.33 °C per decade (in the case of the SSP5-8.5 scenario). If these projections come true, they should be considered unfavorable, and such a situation will require taking into account changes in the thermal regime in the functioning of the Baltic Sea. More broadly, this simple yet effective method for predicting thermal conditions may be applied in interdisciplinary research as well as in the management of coastal marine zones. Full article

The Baltic Dry Index (BDI) measures the cost of transporting dry bulk commodities such as coal, iron ore, and grain. As a key indicator of global trade, supply chain dynamics, and overall economic activity, accurate short-term forecasting of the BDI is crucial. This paper compares six univariate methods to obtain a more precise short-term BDI prediction model, providing valuable insights for decision-makers. The six forecasting techniques include Grey Forecast, ARIMA, Support Vector Regression, LSTM, GRU and EMD-SVR-GWO. Model performance is evaluated using three common metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). Our findings reveal that the novel EMD-SVR-GWO model outperforms the other univariate methods, demonstrating superior accuracy in forecasting monthly BDI trends. This study contributes to improved BDI prediction, aiding managers in strategic planning and decision-making. Full article

Accurate early-warning forecasting of corporate financial distress remains a critical challenge due to nonlinear financial relationships, severe data imbalance, and the high operational costs of false alarms in risk-monitoring systems. This study proposes an explainable voting ensemble framework for early-warning forecasting of corporate financial distress using lagged accounting-based financial information. The proposed framework integrates heterogeneous base learners, including Decision Tree, Neural Network, and k-Nearest Neighbors models, and is evaluated using financial statement data from 752 publicly listed firms in Thailand, comprising sixteen financial ratios across six dimensions: liquidity, operating efficiency, debt management, profitability, earnings quality, and solvency. To ensure robustness under imbalanced and rare-event conditions, the study employs feature selection, data normalization, stratified cross-validation, resampling techniques, and repeated validation procedures. Empirical results demonstrate that the proposed Voting Ensemble delivers a precision-oriented and decision-relevant forecasting profile, outperforming classical classifiers and maintaining greater early-warning reliability when benchmarked against advanced tree-based ensemble models. Probability-based evaluation further confirms the robustness and calibration stability of the proposed framework under repeated cross-validation. By adopting a forward-looking, early-warning perspective and integrating ensemble learning with explainable machine learning principles, this study offers a transparent and scalable approach to financial distress forecasting. The findings offer practical implications for auditors, investors, and regulators seeking reliable early-warning tools for corporate risk assessment, particularly in emerging market environments characterized by data imbalance and heightened uncertainty. Full article

Improving the accuracy of climate and long-range meteorological forecasts is an important objective for many economic sectors: agriculture, energy and utilities, transportation and logistics, construction, disaster risk management, insurance and finance, retail, tourism and leisure. Traditional physical models face limitations at ultra-long lead times, which motivates the development of empirical–statistical approaches, including those leveraging deep learning techniques. In this study, using ERA5 reanalysis data and archives of major climate indices for the period 1950–2024, we examine statistical relationships between climate indices associated with large-scale atmospheric and oceanic patterns in the Northern Hemisphere and surface air temperature anomalies in selected mid- and high-latitude regions. The aim is to assess the predictive skill of these indices for seasonal temperature anomalies within empirical forecasting frameworks. To this end, we employ cross-correlation and cross-spectral analyses, as well as regression modeling. Our findings indicate that the choice of the most informative predictors strongly depends on the target region and season. Among the major indices, AMO and EA/WR emerge as the most informative for forecasting purposes. The Niño 4 and IOD indices can be considered useful predictors for the Eastern Arctic. Notably, the strongest correlations between the AMO, EA/WR, Niño 4, and IOD indices and surface air temperature occur at one- to two-year lags. To illustrate the predictive potential of the four selected indices, several multiple regression models were developed. The results obtained from these models confirm that the chosen set of indices effectively captures the main sources of variability relevant to seasonal and interannual temperature prediction across the analyzed regions. In particular, approximately 64% of the forecasts have errors less than 0.674 times the standard deviation. Full article

AI Recommender Systems (RecSys) function as personalised forecasting engines, predicting user preferences to reduce information overload. However, the efficacy of these systems is often bottlenecked by the “Last Mile” of forecasting: the end-user’s willingness to adopt and rely on the prediction. While the existing literature often assumes that algorithmic accuracy (e.g., low RMSE) automatically drives utilisation, empirical evidence suggests that users frequently reject accurate forecasts due to a lack of trust or cognitive friction. This study challenges the utilitarian view that users adopt systems simply because they are useful, instead proposing that sustainable adoption requires a state of Cognitive Absorption—a psychological flow state enabled by the Cognitive Economy of trust. Grounded in the Motivation–Opportunity–Ability (MOA) framework, we developed the Trust–Absorption–Intention (TAI) model. We analysed data from 366 users of a major predictive platform using Partial Least Squares Structural Equation Modelling (PLS-SEM). The Disjoint Two-Stage Approach was employed to model the reflective–formative Higher-Order Constructs. The results demonstrate that Cognitive Trust (specifically the relational dimensions of Benevolence and Integrity) operates via a dual pathway. It drives adoption directly, serving as a mechanism of Cognitive Economy where users suspend vigilance to rely on the AI as a heuristic, while simultaneously freeing mental resources to enter a state of Cognitive Absorption. Affective Trust further drives this immersion by fostering curiosity. Crucially, Cognitive Absorption partially mediates the relationship between Cognitive Trust and adoption intention, whereas it fully mediates the impact of Affective Trust. This indicates that while Cognitive Trust can drive reliance directly as a rational shortcut, Affective Trust translates to adoption only when it successfully triggers a flow state. This study bridges the gap between algorithmic forecasting and behavioural adoption. It introduces the Cognitive Economy perspective: Trust reduces the cognitive cost of verifying predictions, allowing users to outsource decision-making to the AI and enter a state of effortless immersion. For designers of AI forecasting agents, the findings suggest that maximising accuracy may be less effective than minimising cognitive friction for sustaining long-term adoption. To solve the cold start problem, platforms should be designed for flow by building emotional rapport and explainability, thereby converting sporadic users into continuous data contributors. Full article

To address the nonlinear nature of exchange rates where drivers vary by time horizon, this paper proposes a CEEMDAN-PE-CatBoost-SHAP framework. Analyzing USD/CNY data (2012–2024), we decomposed rates into high, medium, and low frequencies to bridge machine learning with economic interpretability. Empirical results revealed distinct frequency-dependent drivers: high-frequency fluctuations depend on market sentiment; medium-frequency variations follow Fed policies; and low-frequency trends reflect fundamentals like gold prices. SHAP analysis provides transparent attribution of these factors. This multi-scale approach isolates heterogeneous drivers, offering policymakers and investors a nuanced paradigm for managing currency risks. The study significantly clarifies how different economic factors shape exchange rate dynamics across varying time scales. Full article

We introduce the Semiparametric Time Series Regression with Mixed Additive Spline Fourier (STSR–MASF) model as an innovative approach for analyzing time series data with complex patterns. The model combines the flexibility of the spline estimator in capturing nonlinear variations across specific sub-intervals and the strength of the Fourier series in representing periodically recurring patterns. Within the semiparametric regression framework, STSR–MASF integrates both linear parametric and nonparametric components, with the optimal number of knots and oscillations determined using the Generalized Cross-Validation (GCV) criterion. The model was trained and tested using Earth’s skin temperature data from the National Aeronautics and Space Administration (NASA) MERRA-2 for East Kalimantan, Indonesia, a tropical rainforest region. The results demonstrate that the STSR–MASF model provides more accurate estimations and forecasts compared to six previous methods proposed in earlier studies with highly accurate predictions. This innovation not only offers methodological advancements in nonlinear time series modeling, but also contributes practical insights into understanding variations in Earth’s skin temperature in tropical regions, supporting broader efforts toward global climate change mitigation. Full article

Accurate air quality forecasting remains challenging due to persistent biases in chemical transport models. Addressing this challenge, the current study develops pollutant-specific deep learning frameworks that correct systematic errors in the Community Multiscale Air Quality (CMAQ) simulations of nitrogen dioxide (NO₂) and coarse particulate matter (PM₁₀) over California. Building upon a previous study on ozone bias correction, a hybrid CNN–Attention–LSTM architecture is adapted, and a weighted Huber loss function is introduced for PM₁₀ to enhance the detection of extreme pollution events through a gated tail-weighting mechanism. Using data from twenty EPA monitoring stations (ten per pollutant) for 2010–2014, the proposed approach achieves substantial performance gains over the CMAQ baseline. For NO₂, RMSE decreases by ~51% with an average systematic bias reduction of ~80% and a random error reduction of ~42%. For PM₁₀, RMSE improves by ~49% while the systematic and random errors decrease by ~94% and ~33%, respectively. The PM₁₀ model also shows high consistency with observations (Index of Agreement improvement of ~105%) and a strong ability to capture peak events (F1 score improvement of ~270%), while the NO₂ model achieves large gains in explanatory power (R² improvement averaging ~816%). Both pollutants also demonstrate enhanced temporal agreement between predictions and observations, as confirmed by the Dynamic Time Warping analysis (NO₂: ~55%, PM₁₀: ~58%). These results indicate that pollutant-specific loss functions and architectural tuning can significantly improve both accuracy and event sensitivity, offering a transferable framework for bias correction across multiple pollutants and regions. Full article

This study employed tree-based machine learning (ML) algorithms to predict low-level wind shear (LLWS) at Jeju International Airport (ICAO: RKPC). Hourly meteorological data from 47 observation stations across Jeju Island, collected between 2019 and 2023, were split into training (60%), validation (20%), and test (20%) sets to develop individual prediction models for lead times ranging from 1 to 6 h. A probabilistic prediction model was developed by assigning weights to individual models according to their true skill statistic performance. Validation using an independent 2024 dataset showed that the light gradient boosting machine-based probabilistic model exhibited the highest predictive performance, achieving an area under the receiver operating characteristic curve of 0.883. The Shapley additive explanation analysis identified wind components (U, V) as key variables, contributing over 50%, with the significance of pressure and temperature slightly increasing over long-term prediction times (4–6 h). In addition, spatial analysis revealed that nearby airport stations were more influential for short-term prediction times (1–2 h), whereas Mount Halla and offshore stations north of the airport gained greater influence for medium-to long-term prediction times (3–6 h). The ML-based LLWS prediction model offers high accuracy and interpretability, supporting stepwise warning systems and aiding aviation decision-making. Full article

Short-term load forecasting is fundamental for the effective and reliable operation of power systems. Very accurate forecasting methods often involve complex hybrid approaches that combine statistical, physical, and/or intelligent techniques. In this work, we present an innovative, clear, and effective methodology for short-term hourly peak load forecasting that is both simple and highly accurate. The methodology is based on the load forecast used for electricity market purposes, together with fine-tuning dynamic estimation. As a case study, the methodology was applied and tested in Mexico’s interconnected power system. It was implemented across various regions and at both regional and load-\ zone levels of this interconnected power system and, even under a variety of standard and extreme load conditions, achieved outstanding results. Full article

This study compares Logit, Probit, Extreme Value, and Artificial Neural Network (ANN) models using data from 2012 to 2024 in the Taiwan electronics industry. ANN outperforms traditional models, achieving 98% accuracy in predicting financial distress. Two robust distress signals are identified: Return on Assets (threshold: 7.03%) and Total Asset Growth (threshold: −9.05%). The nonlinear impacts of financial distress on variables are analyzed, with a focus on contextual considerations in decision-making. These findings bring attention to the importance of utilizing advanced techniques like ANN for improved predictive accuracy, offering profound clarification for risk assessment and management. Full article