Forecasting PM2.5 concentrations remains a challenging data science problem because of strong nonlinearity, temporal dependence, and pronounced nonstationarity associated with seasonal and episodic pollution events. This study presents a comparative evaluation of recurrent neural network architectures for one-day-ahead PM2.5 forecasting using daily observations from four provinces in northern Thailand with varying pollution dynamics. Standard RNN, LSTM, and GRU models were developed within a unified forecasting framework using historical PM2.5 concentrations and meteorological variables as predictors. Model performance was evaluated on an independent test dataset using R2, RMSE, and MAE, together with Diebold–Mariano tests based on forecast error series. The experimental results indicate that the GRU model provides more robust forecasting performance under conditions of strong volatility and nonstationary behaviour, while the LSTM and RNN models remain competitive in comparatively more stable environments. No single model consistently dominated across all provinces, indicating that forecasting effectiveness depends strongly on the temporal characteristics of local air pollution dynamics rather than on architectural complexity alone. While all models capture the overall seasonal structure of PM2.5 variation, extreme pollution peaks are systematically underestimated partly because MSE-based training biases predictions toward the central tendency of the data distribution. However, gated architectures show improved responsiveness to abrupt concentration changes relative to the standard RNN. These findings highlight the importance of matching recurrent architectural design to the temporal regime of the target environment.
Crypto perpetual futures embed liquidation risk in one chain: leverage and funding move the margin boundary, the mark determines when a crossing is observed, and executable depth determines the concession paid after detection. The primary forecasting question is how to quantify both the probability of an isolated-margin boundary breach and the loss hidden by delayed or smoothed detection. This paper develops a first-passage density-forecasting framework in which the executable price is observed through a delayed or smoothed mark, funding is a persistent collateral drain, and liquidation occurs when isolated-margin surplus reaches its maintenance boundary. The output is a joint predictive distribution: a horizon-specific probability of a boundary breach and, conditional on detection, a distribution of catch-up loss. Under a fixed delay, expected overshoot is σδ/2π, and bad-debt probability is a Gaussian tail governed by the latency-to-margin ratio σδ/m. Its leverage independence is exact only in the constant-maintenance, fixed-non-price-drain benchmark. For time-weighted-average marks, fixed-time variance reduction does not imply liquidation-time safety. A stationary-downcrossing approximation size-biases the stale error in the adverse direction, producing a mean overshoot about 1.6 times the same-window fixed-delay value. A rolling comparison with historical simulation scores model-consistent margin-breach forecasts from public price and funding paths. The structural forecast has lower Brier scores at 10× over 24-, 72- and 168-hour horizons and across the 24-hour grid, but historical simulation performs better for one-week forecasts at 20× and 50×. The evidence supports a conditional risk-forecasting use of the framework, while not establishing uniform forecast dominance.
Tourism-demand forecasting overwhelmingly targets aggregate volumes, leaving the question of where demand will come from largely unaddressed. This paper forecasts that question directly. We make three contributions. First, we apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to guest origin composition in large-scale platform booking data, which to our knowledge is the first use of Bayesian compositional time-series methods on booking-origin shares across multiple global destination regions. Second, we introduce seasonal structure in the Dirichlet precision parameter, and we isolate its contribution through an ablation against an otherwise identical constant-precision specification: seasonal precision lowers mean absolute error in all four destination regions, by between 13% and 35%. Third, the forecast target is the monthly composition of bookings indexed by booking date rather than stay date, which makes it observable ahead of realized arrivals and therefore usable for decisions with long lead times. Using proprietary Airbnb reservation data spanning 2017–2025 across four destination regions, we document substantial pandemic-era shifts in booking composition with heterogeneous recovery patterns. In rolling-origin evaluation, BDARMA achieves the lowest forecast error for EMEA, the most compositionally diverse region, reducing mean absolute error by 27% relative to naïve forecasts (p<0.001). Performance elsewhere is mixed: simple benchmarks remain hard to beat, and exponential smoothing on isometric log-ratio-transformed data attains the lowest error averaged across the four regions. The EMEA pattern suggests that direct compositional modeling is most valuable where several origin markets hold material shares, although four destination regions are too few to establish this as a general rule. The methodology yields probabilistic forecasts of source market shares that can inform marketing allocation, concentration-risk monitoring, and forward-looking operational planning.
Understanding which segments of the economy drive aggregate stock market movements is central to risk management. This study traces how the economic drivers of the Standard & Poor’s 500 Index (S&P 500) changed across two episodes: the 2020 COVID-19 shock and the 2025 technology-led period, using eleven sector indices and nine deep learning architectures. During COVID-19, forecasting power concentrated in Consumer Discretionary, Health Care, and Industrials before reorganizing sharply around Information Technology, consistent with a disruptive break. In 2025, Information Technology and market momentum dominated throughout, with no comparable reorganization, consistent with a gradual adjustment rather than a disruptive shift. This distinction, invisible from accuracy metrics alone (Gated Recurrent Unit: Mean Absolute Percentage Error = 3.41% and 2.16%), shows that information-concentration diagnostics can complement forecast-accuracy and risk-monitoring frameworks.
This study develops a rigorous, leakage-free forecasting framework for monthly Robusta coffee prices using historical observations from January 1975 to December 2025. A comprehensive set of explanatory variables is constructed from lagged coffee prices, moving averages, logarithmic returns, rolling volatility, and exogenous variables such as the Oceanic Niño Index (ONI), the U.S. Dollar Index, and Brent crude oil prices. To ensure methodological fairness, all predictors are generated exclusively from information available at the forecast origin, and all competing models are evaluated under a unified expanding-window walk-forward validation framework. Seven forecasting models are compared: Naïve, Exponential Smoothing (ETS), ARIMA, ARIMAX, Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). Forecasting performance is evaluated using R2, RMSE, MAE, and MAPE, while Taylor diagrams and the Diebold–Mariano test are employed to assess model agreement and differences in predictive accuracy. The results show that XGBoost achieves the highest forecasting accuracy (R2 = 0.956, RMSE = 0.264), followed closely by the Naïve (R2 = 0.954, RMSE = 0.271) and ARIMA (R2 = 0.954, RMSE = 0.270) benchmarks, whereas ARIMAX and ETS provide comparable performance and the deep learning models (LSTM and GRU) produce substantially larger prediction errors. Feature importance analysis further indicates that the first lag of coffee price is the dominant predictor, accounting for approximately 94% of the predictive gain in XGBoost. Overall, the findings demonstrate that rigorous leakage-free validation is essential for reliable forecasting research and that, for monthly Robusta coffee prices, increased model complexity does not necessarily yield superior predictive performance.
Air-quality forecasting models are often compared by architecture, although reported skill also depends on the pollutant, monitoring density, forecast horizon, predictor latency, validation design, and deployment objective. We conducted a systematic mapping review of 533 unique records published between 2000 and 15 June 2026; 409 met the forecasting eligibility criteria. The evidence was analysed in two layers: a metadata-derived map of the full corpus and a targeted full-text synthesis of representative studies. The non-exclusive metadata categories show that general machine learning or benchmark studies were most common (n=207), followed by recurrent deep learning (n=109), hybrid or decomposition methods (n=60), Transformer or attention models (n=51), tree ensembles (n=48), CNN/ConvLSTM models (n=43), classical statistical methods (n=26), graph neural networks (n=13), and physics-informed or CTM-coupled methods (n=7). These counts describe topical prevalence, not comparative effectiveness. The main contribution is a decision framework that links the forecasting setting to a defensible starting model, the evidence available for that model family, and the minimum validation needed to support temporal, spatial, or external generalisation. The synthesis favours transparent statistical and tabular baselines for short or sparse single-station records; spatial deep models only when network geometry and leave-site-out testing support them; and CTM-coupled postprocessing when operational physical fields are available at issue time. Diffusion and foundation models remain promising but unevenly validated for pollutant forecasting. A leakage-safe daily PM2.5 case study in Naples illustrates the practical consequence: model rankings change with the metric, and every fitted model underestimates the highest 5% of concentrations.
Forecasting research repeatedly finds that simple methods can match or beat complex ones out of sample. We test this in a safety-critical domain, near-real-time prediction of fire hazard state and structural damage, using a simulator-grounded benchmark: high-fidelity computational fluid dynamics (Fire Dynamics Simulator, FDS) provides reference data, an FDS-calibrated zone model (CFAST) generates a large corpus cheaply, and the temperature trajectories drive a finite-element model (OpenSees) and a HAZUS/Eurocode-informed damage rule. Under one protocol we compare simple, deep (PatchTST, TimesNet), and physics-informed (PINN, PIKAN) forecasters. Complex models do not dominate: a small corpus lets parsimonious models approach best accuracy, and physics helps mainly when data are scarce (crossover near twenty scenarios). We propose CD-PINN, which identifies a data-optimal reduced-order physics residual from the corpus by physics-guided regression over a candidate library and uses it as the physics constraint. This lifts a per-event physics model to the accuracy of corpus-trained forecasters while staying interpretable. On 26 laboratory-fire experiments, however, in-distribution rankings do not transfer: the large accuracy spread collapses to near-parity, so a leaderboard poorly predicts laboratory-fire accuracy. For downstream damage, we further show that the label definition, not the model class, sets the achievable ceiling.
Rainfall prediction models often lose skill when transferred across regions, particularly in data-sparse settings where local recalibration is not feasible. This study investigates whether topographically analogous landscapes exhibit consistent patterns of model portability in next-day rainfall occurrence prediction across contrasting climates. Random Forest and Logistic Regression classifiers trained on multi-year daily atmospheric data were evaluated across three hydrogeomorphic classes, Alluvial/Valley, Delta/Marsh, and Coastal Plain, using paired temperate sites in the United States and tropical sites in Malaysia. Portability was quantified using Transferability, representing a model’s ability to export predictive skill, and Adaptability, representing a site’s receptivity to externally trained models. Performance, sensitivity, and stability metrics were synthesized within a Behaviour Grid to support systematic interpretation. Results reveal a robust terrain-driven hierarchy: Delta/Marsh models are the strongest exporters, Alluvial/Valley sites the most adaptable receivers, and Coastal Plain sites are stable generalists. This general hierarchy is preserved across climates and years when evaluated using Area Under the Receiver Operating Characteristics Curve, indicating strong terrain structuring of portability, although fixed-threshold performance metrics show sensitivity to temporal variability. These findings indicate that terrain class provides a useful basis for evaluating cross-regional model transfer, supporting more informed regionalisation and deployment decisions across heterogeneous environmental settings.
This paper proposes a novel variational mode decomposition (VMD)-enhanced ensemble framework for tourism demand forecasting, which achieves competitive predictive performance. The core innovation is a structured hybrid methodology that first employs VMD to decompose complex temporal signals, from which multiple interpretable features—encoding trend, volatility, and momentum characteristics—are extracted. These enriched features are then processed by an intelligently weighted ensemble of three diverse models: support vector regression (SVR), gradient boosting regression (GBR), and random forest (RF). An adaptive weighting mechanism dynamically optimizes the contribution of each base model, enhancing robustness and accuracy. Comprehensive experiments demonstrate the framework’s superior effectiveness. Compared to strong contemporary benchmarks such as CNN-LSTM, our approach achieves significant error reduction: an RMSE of 0.837 (16.29% improvement), an MAE of 0.584 (12.19% improvement), and a MAPE of 15.42% (13.38% improvement). These results validate that the integration of VMD-based feature engineering with an adaptive multi-model ensemble effectively captures complex temporal dynamics and mitigates individual model limitations.
High classification performance in financial-risk early-warning research may reflect recovery of a constructed contemporaneous rating rule rather than prediction of an independent future outcome. This study distinguishes these two forms of evidence through a controlled synthetic-panel experiment situated in the context of higher education institutions (HEIs). Task 1 is a contemporaneous positive-control audit in which the four components defining a deterministic three-class rating are supplied to the classifiers. Task 2 uses predictors measured at year t−1 to classify the rating at year t through strict rolling-origin evaluation under prespecified Weak, Moderate, and Strong temporal-persistence conditions. In Task 1, the four fitted classifiers achieved held-out Macro-F1 values of 0.9968–1.0000, demonstrating near-complete recovery of the disclosed rating rule but providing no prospective forecasting evidence. In Task 2, the best mean annual Macro-F1/Macro-AUC increased from 0.480/0.696 under Weak persistence to 0.549/0.747 under Moderate persistence and 0.633/0.824 under Strong persistence. The Weak–Moderate–Strong ordering was observed for both primary metrics across all four fitted classifiers, while higher model complexity provided no consistent advantage within the disclosed synthetic mechanism. These findings confirm contemporaneous rule recoverability and sensitivity to deliberately embedded temporal persistence only within the controlled experiment. They do not establish predictive validity, transportability, or decision benefit in real HEIs. The study contributes a reproducible task-to-claim approach that aligns target construction, predictor–target overlap, information timing, rolling-origin evaluation, probability quality, and model performance with the inferences that the resulting evidence can legitimately support. Real-world validation would require source-indexed longitudinal HEI data, independently adjudicated post-origin outcomes, verified information-availability dates, external testing, and prospective decision evaluation.
This study examines whether centralized-exchange flows improve one-day-ahead forecasts of SOL return direction. Daily on-chain analysis (OCA) predictors are constructed from more than 41 million transfers above 1 SOL involving 101 labeled centralized-exchange hot wallets. Seven feature sets are evaluated using CatBoost, XGBoost, Random Forest, LSTM, and BiLSTM across four expanding-origin holdouts, with Elastic Net Logistic Regression as a linear benchmark. Technical analysis (TA) achieves the strongest average classification and transaction-cost-adjusted trading performance. OCA performs weakly alone and provides no stable average improvement when added to Baseline or TA. Selected flow- and volume-related subblocks outperform unrestricted OCA on individual descriptive metrics, but none achieves positive average trading performance. Observable Solana exchange flows therefore provide no robust standalone or incremental forecasting advantage beyond conventional predictors.
Large language models are increasingly used for tasks that require prediction, interpretation, and decision support, yet their behavior in complex sports forecasting is still not well understood. This study evaluates how different large language models perform in predicting the FIFA World Cup 2026 under several forecasting settings. The benchmark covers three levels of tournament prediction: the group stage, the knockout stage, and the final outcome of the competition. The evaluation includes proprietary models, cloud hosted models, and open weight models, tested with standard prompting, reasoning based inference, web search support, and agent based forecasting workflows. The analysis goes beyond simple winner prediction and examines structural validity, qualification accuracy, hallucination rate, consistency, forecast plausibility, and agreement with mainstream football expectations. The results indicate that access to current external information has the strongest effect on forecasting reliability. In the OpenAI based experiments, web supported agent configurations increased structural validity from 58.50 to 90.87 and reduced hallucinations by 78.7 percent. A similar improvement was observed in the Ollama and cloud model group, where web access increased validity from 53.39 to 97.04 and reduced hallucinations by 75.7 percent. Reasoning improved the internal logic of several forecasts, but when it was used without external grounding, it sometimes produced confident but unsupported predictions. These findings suggest that reasoning alone is not sufficient for tournament forecasting when the task depends on current squads, recent performance, injuries, rankings, and evolving football context. The best results were achieved when models combined structured reasoning with access to up to date information. Overall, this study provides a reproducible evaluation framework for large language model based sports forecasting and shows how grounding, reasoning, and model design influence prediction quality in a complex international tournament setting.
The study develops and empirically evaluates a forecasting-orientated structural model in which future Bitcoin historical volatility is modelled as being associated with market sentiment and blockchain fundamentals through market uncertainty. Market Sentiment (MS) is specified as a behavioural construct, Blockchain Fundamentals (BF) as network conditions, and Market Uncertainty (MU) as an endogenous regime construct that consolidates signals shaping historical volatility at t+1. Using 262 weekly observations from January 2021 to January 2026, the analysis applies partial least squares structural equation modelling (PLS-SEM) with formative constructs and a forward-dated volatility target to preserve temporal ordering. Paths are evaluated with bootstrapping, effect sizes, and mediation analysis, while predictive performance is assessed using PLSpredict, the cross-validated predictive ability test (CVPAT), benchmark-based comparison, and Diebold-Mariano (DM) tests. MU emerges as the dominant predictor of Future Historical Volatility, denoted as HV(t+1) in the structural model (beta = 0.864, p-value < 0.001; f(2) = 2.036). The effect of BF is largely indirect, with 91.02% of the total effect transmitted via uncertainty, indicating indirect-only mediation. The model explains substantial variation in HV(t+1) (R-2 = 0.791) and shows predictive relevance (Q(2) predict = 0.287), while the benchmark-based results indicate mixed but competitive forecasting performance relative to persistence-based and econometric alternatives. These findings are consistent with a regime-based interpretation of Bitcoin volatility and highlight the explanatory and predictive relevance of an integrated behavioural-network-uncertainty architecture.
Highlights What are the main findings? The VMD-Augmented BiLSTM framework achieves Pearson correlation of 0.821 +/- 0.016 and a Standard Deviation Ratio (StdR) of 1.091 +/- 0.060 on daily rubber price changes across five random seeds, with directional accuracy of 82.5% +/- 1.8% and down-direction recall of 78.1% +/- 5.5% on a leakage-free held-out test set. Directional accuracy alone is an insufficient evaluation metric for differenced commodity series: Vanilla LSTM attains directional accuracy of 82.29% +/- 0.00 across five seeds, statistically indistinguishable from that of the proposed model, yet exhibits variance collapse (StdR = 0.210 +/- 0.007 vs. 1.091 +/- 0.060), confirming the need for variance-sensitive co-primary metrics. What are the implications of the main findings? Appending VMD components directly as input features-rather than summing independent mode forecasts-preserves multi-scale frequency information and prevents variance collapse (per-IMF conventional ablation: StdR approximate to 0.20) on differenced price series. Ablation analysis indicates that the bidirectional LSTM pathway is the primary architectural driver of performance; the additional Transformer self-attention pathway offers no consistent gain in this context.Highlights What are the main findings? The VMD-Augmented BiLSTM framework achieves Pearson correlation of 0.821 +/- 0.016 and a Standard Deviation Ratio (StdR) of 1.091 +/- 0.060 on daily rubber price changes across five random seeds, with directional accuracy of 82.5% +/- 1.8% and down-direction recall of 78.1% +/- 5.5% on a leakage-free held-out test set. Directional accuracy alone is an insufficient evaluation metric for differenced commodity series: Vanilla LSTM attains directional accuracy of 82.29% +/- 0.00 across five seeds, statistically indistinguishable from that of the proposed model, yet exhibits variance collapse (StdR = 0.210 +/- 0.007 vs. 1.091 +/- 0.060), confirming the need for variance-sensitive co-primary metrics. What are the implications of the main findings? Appending VMD components directly as input features-rather than summing independent mode forecasts-preserves multi-scale frequency information and prevents variance collapse (per-IMF conventional ablation: StdR approximate to 0.20) on differenced price series. Ablation analysis indicates that the bidirectional LSTM pathway is the primary architectural driver of performance; the additional Transformer self-attention pathway offers no consistent gain in this context.Abstract This study examines whether decomposition-based deep learning forecasts of daily changes in natural rubber prices can appear directionally accurate while failing to preserve the dispersion of the target series-a failure mode that conventional accuracy metrics cannot detect. Using daily RSS3 FOB price changes in the period 2018-2026, a VMD-Augmented BiLSTM forecasting design is employed as the empirical vehicle for testing this question. Forecasts are evaluated jointly through Pearson correlation, directional accuracy, class-conditional recall, and the Standard Deviation Ratio (StdR), with StdR serving as a diagnostic for variance collapse on differenced series. The deployed model appends all Variational Mode Decomposition (VMD) components directly to the economic feature matrix and feeds the augmented sequence into a bidirectional LSTM encoder with temporal attention; VMD is fitted using an expanding-window procedure to prevent information leakage. The design is compared to a conventional per-IMF decomposition-forecast pipeline, a Vanilla LSTM, ARIMA(2,0,2), and a dual-pathway BiLSTM-Transformer control. On a 175-observation deduplicated test set, the deployed model attains Pearson correlation of r=0.821 +/- 0.016, directional accuracy of 82.5%+/- 1.8%, and StdR =1.091 +/- 0.060 across five random seeds. The Vanilla LSTM baseline attains directional accuracy of 82.29%+/- 0.00-statistically indistinguishable from that of the deployed model-yet exhibits variance collapse (StdR =0.210 +/- 0.007), confirming that DA alone cannot distinguish predictive skill grounded in conditional dynamics from forecasts that merely reproduce the unconditional sign distribution. The principal contribution is methodological: A variance-sensitive evaluation protocol that distinguishes forecast skill grounded in conditional dynamics from directional but underdispersed predictions, demonstrated across three empirically distinct mechanisms by which variance collapse arises in this setting.
Highlights What are the main findings? The accuracy of the developed wind speed forecasting model based on a Gaussian process (GP) for residual modelling largely depends on the local wind regime. The GP model improved forecast accuracy by up to 16.3% at high-elevation inland stations. In contrast, the model was ineffective at the low-elevation coastal stations. What are the implications of the main findings? The success of sophisticated residual modelling techniques, including the Gaussian process model, cannot be generalised and should be adapted to particular geographical and meteorological regimes. The forecasting process should account for the clustering of monitoring stations by geographical location and elevation, e.g., inland or coastal areas.Highlights What are the main findings? The accuracy of the developed wind speed forecasting model based on a Gaussian process (GP) for residual modelling largely depends on the local wind regime. The GP model improved forecast accuracy by up to 16.3% at high-elevation inland stations. In contrast, the model was ineffective at the low-elevation coastal stations. What are the implications of the main findings? The success of sophisticated residual modelling techniques, including the Gaussian process model, cannot be generalised and should be adapted to particular geographical and meteorological regimes. The forecasting process should account for the clustering of monitoring stations by geographical location and elevation, e.g., inland or coastal areas.Abstract Accurate wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it directly influences generation scheduling, grid stability, and energy market operations. Forecast errors can lead to significant economic losses, including increased balancing costs, inefficient dispatch of backup generation, and penalties in electricity markets. However, wind behaviour is highly complex due to the influence of synoptic weather systems, terrain variability, and turbulence, which makes accurate prediction particularly challenging. This paper proposes a hybrid modelling framework that combines a linear regression mean model with Gaussian process (GP) residual modelling to improve forecast accuracy. Monitoring stations were grouped based on geographic coordinates and elevation, with cluster validation using the Hopkins statistic and silhouette analysis. The results show that for high-elevation inland stations (cluster 2), GP residual modelling improves forecast accuracy by up to 16.3%. In contrast, for low-elevation coastal stations (cluster 1), the GP approach does not yield improvements, indicating that its effectiveness depends strongly on the underlying wind regime.
Highlights What are the main findings? Economically grounded data preparation substantially improves the predictive performance of financial distress models across most classification methods, with average gains of approximately 15.6 percentage points in accuracy and 26.9 percentage points in specificity across all six modelling techniques examined. The contribution of data quality to predictive performance amounts to roughly half the performance variation attributable to algorithm choice, supporting the "garbage in, garbage out" principle: models trained on raw, unprocessed financial data consistently underperform their counterparts trained on cleaned and validated data under identical modelling conditions, confirming that input data quality is a major but underexplored driver of model reliability. What are the implications of the main findings? For researchers, preprocessing decisions, including economic plausibility screening, missing value treatment, and class balancing, should be treated as substantive methodological choices and reported transparently, as they materially affect model outcomes and the comparability of findings. For practitioners in banking, auditing, and corporate risk management, structured data validation and economically grounded preprocessing should be considered integral components of financial distress model development, as algorithm optimisation alone is insufficient to achieve reliable predictive performance when input data quality is poor.Highlights What are the main findings? Economically grounded data preparation substantially improves the predictive performance of financial distress models across most classification methods, with average gains of approximately 15.6 percentage points in accuracy and 26.9 percentage points in specificity across all six modelling techniques examined. The contribution of data quality to predictive performance amounts to roughly half the performance variation attributable to algorithm choice, supporting the "garbage in, garbage out" principle: models trained on raw, unprocessed financial data consistently underperform their counterparts trained on cleaned and validated data under identical modelling conditions, confirming that input data quality is a major but underexplored driver of model reliability. What are the implications of the main findings? For researchers, preprocessing decisions, including economic plausibility screening, missing value treatment, and class balancing, should be treated as substantive methodological choices and reported transparently, as they materially affect model outcomes and the comparability of findings. For practitioners in banking, auditing, and corporate risk management, structured data validation and economically grounded preprocessing should be considered integral components of financial distress model development, as algorithm optimisation alone is insufficient to achieve reliable predictive performance when input data quality is poor.Abstract Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix-based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models.
Highlights What are the main findings? A regime-aware statistical learning framework integrating Kalman filtering, Markov-switching dynamics, and nonlinear recurrent learning improves stock-index forecasting by combining noise reduction, latent-state identification, and nonlinear temporal adaptation. Regime-conditioned forecasting structures consistently outperform traditional and pure deep-learning models, with latent-state information providing the dominant contribution to predictive accuracy and economic value. What are the implications of the main findings? Predictive accuracy alone does not ensure trading profitability; incorporating regime information is essential for translating forecasts into economic value. Model effectiveness varies across market environments, with regime-based forecasting capturing most predictability in developed markets, while hybrid statistical-learning structures provide additional gains in more volatile or emerging markets.Highlights What are the main findings? A regime-aware statistical learning framework integrating Kalman filtering, Markov-switching dynamics, and nonlinear recurrent learning improves stock-index forecasting by combining noise reduction, latent-state identification, and nonlinear temporal adaptation. Regime-conditioned forecasting structures consistently outperform traditional and pure deep-learning models, with latent-state information providing the dominant contribution to predictive accuracy and economic value. What are the implications of the main findings? Predictive accuracy alone does not ensure trading profitability; incorporating regime information is essential for translating forecasts into economic value. Model effectiveness varies across market environments, with regime-based forecasting capturing most predictability in developed markets, while hybrid statistical-learning structures provide additional gains in more volatile or emerging markets.Abstract This study proposes a hybrid forecasting framework that integrates Kalman Filtering (KF), Markov Switching (MS), and nonlinear recurrent learning for stock-index prediction. The KF component smooths short-term price noise, the MS model identifies latent return-volatility regimes, and the LSTM/GRU components learn nonlinear temporal patterns from regime-conditioned information. The framework is evaluated using the CSI 300, S&P 500, and Nikkei 225 indices through forecasting-accuracy measures, Bootstrap Diebold-Mariano tests with Modified Bayes Factor evidence, out-of-sample trading simulations, and robustness checks. The empirical results show that regime conditioning is the primary source of forecasting and economic improvement. KF-MS-LSTM performs best for the CSI 300 and Standard MS performs strongest for the S&P 500, while KF-MS-LSTM and KF-MS-GRU are more competitive for the Nikkei 225. In contrast, models without regime information, including pure LSTM/GRU and the standalone Transformer, generally exhibit weaker forecasting and trading performance. The findings suggest that latent market-state information is more important than neural-network complexity alone for robust financial forecasting, while the incremental value of Kalman filtering and recurrent learning remains market dependent. Overall, the results support regime-aware forecasting as an interpretable and economically meaningful approach for stock-index prediction under heterogeneous market environments.
Highlights What are the main findings? Updating the WRF model with the high-resolution ESA WorldCover 2021 LULC dataset significantly improved predictions for 2 m temperature, relative humidity, and 10 m wind speed across 85% of the evaluated sites during the July 2023 Cyprus heatwave. The modernized spatial boundaries effectively restored the urban "thermal memory", allowing the model to successfully capture the deep daytime Urban Cool Island (UCI) effect, nocturnal heat release, and correct systematic underestimations of the nocturnal Planetary Boundary Layer Height (PBLH). What are the implications of the main findings? Integrating highly accurate, static land cover maps intrinsically recalibrates surface energy partitioning, which can partially mitigate the immediate operational need for computationally expensive urban modeling during extreme thermal events. Static boundary updates alone are insufficient to resolve the model's damped thermal inertia or deep-rooted kinetic errors, highlighting the need for future simulations to incorporate dynamic "Green Resilience" parameters such as increased urban vegetation coupled with soil moisture in urban model.Highlights What are the main findings? Updating the WRF model with the high-resolution ESA WorldCover 2021 LULC dataset significantly improved predictions for 2 m temperature, relative humidity, and 10 m wind speed across 85% of the evaluated sites during the July 2023 Cyprus heatwave. The modernized spatial boundaries effectively restored the urban "thermal memory", allowing the model to successfully capture the deep daytime Urban Cool Island (UCI) effect, nocturnal heat release, and correct systematic underestimations of the nocturnal Planetary Boundary Layer Height (PBLH). What are the implications of the main findings? Integrating highly accurate, static land cover maps intrinsically recalibrates surface energy partitioning, which can partially mitigate the immediate operational need for computationally expensive urban modeling during extreme thermal events. Static boundary updates alone are insufficient to resolve the model's damped thermal inertia or deep-rooted kinetic errors, highlighting the need for future simulations to incorporate dynamic "Green Resilience" parameters such as increased urban vegetation coupled with soil moisture in urban model.Abstract The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land-atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15-29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements.
Highlights What are the main findings? Chaos is present in prices, returns, and trading volume changes in cryptocurrencies. Prices, returns, and trading volume changes are nonlinear and deterministic. Prices, returns, and trading volume changes are predictable on daily basis. What are the implications of the main findings? Prior information on nonlinear dynamics and chaos in cryptocurrency data can be considered to implement intelligent forecasting systems. Profits can be generated as cryptocurrency markets are not efficient.Highlights What are the main findings? Chaos is present in prices, returns, and trading volume changes in cryptocurrencies. Prices, returns, and trading volume changes are nonlinear and deterministic. Prices, returns, and trading volume changes are predictable on daily basis. What are the implications of the main findings? Prior information on nonlinear dynamics and chaos in cryptocurrency data can be considered to implement intelligent forecasting systems. Profits can be generated as cryptocurrency markets are not efficient.Abstract Background: Lyapunov exponent has been used in many science and engineering problems to quantify chaos in systems and understand their nonlinear dynamics. In financial engineering and forecasting, evaluation of chaos in financial data helps determine whether the data are predictable and if profits can be generated. The purpose of this study is to examine presence of chaos in cryptocurrency markets. Methods: To examine chaos, Lyapunov exponent is computed from a set of 50 cryptocurrencies and statistical one-sided and two-sided Student-t tests are performed to check if on average the computed Lyapunov exponents are equal, less, or larger than zero. Results: The statistical results reveal strong evidence that prices, returns, and trading volume changes are all chaotic; hence, they show nonlinear and deterministic characteristics. Conclusions: Prices, returns, and trading volume changes in cryptocurrencies could be predicted in the short run; for instance, on a daily basis. In this regard, active traders and investors may implement predictive systems to generate daily profits.
Highlights What are the main findings? Avocado exports show strong long-term growth, while mango exports are volatile due to supply-chain and trade disruptions. Inclusion of production and consumption data in ARIMAX models improves the accuracy of mango export forecasts. What are the implications of the main findings? Australia's export potential is supported by favourable macroeconomic conditions, including rising GDP and stable exchange rates in key markets. To maintain stable export growth, industry planning must balance production increases with export demand, especially for commodities with higher volatility.Highlights What are the main findings? Avocado exports show strong long-term growth, while mango exports are volatile due to supply-chain and trade disruptions. Inclusion of production and consumption data in ARIMAX models improves the accuracy of mango export forecasts. What are the implications of the main findings? Australia's export potential is supported by favourable macroeconomic conditions, including rising GDP and stable exchange rates in key markets. To maintain stable export growth, industry planning must balance production increases with export demand, especially for commodities with higher volatility.Abstract Export supply chains (ESCs) for perishable fruits, such as mangoes and avocados, are shaped by complex supply-demand dynamics and macroeconomic conditions. However, limited forecasting of these dynamics constrains strategic planning and investment in Australia's horticultural sector. This study assesses the longitudinal growth and future potential of mango and avocado exports. To achieve this, the study identifies influential supply-demand dynamics and applies time-series forecasting to understand the export trends. Historical export-import data were analysed for mango and avocado from 1992 to 2024, including volume, value, per capita GDP (Australia and key importing nations), real exchange rate, and real interest rate. Holt's exponential smoothing was used to forecast export trends, supported by unit root testing in RStudio 4.2.3 and model execution in SPSS version 30. ARIMA and ARIMAX models were applied to stationary variables to improve mango export forecasts. The results show that avocado exports follow a strong upward trajectory, while mango exports remain volatile due to logistical inefficiencies and informal trade disruptions. ARIMAX modelling confirmed that production and consumption volumes significantly enhance forecast accuracy. Macroeconomic trends, rising GDP, declining real interest rates, and stable real exchange rates further reinforce Australia's competitive position in the destination markets. The long-run trends in export volume and value suggest that both the mango and avocado sectors hold potential for further export growth, although the higher volatility observed in the avocado series indicates that expansion should be approached cautiously. To sustain this growth, maintaining a balanced relationship between production capacity and export demand, particularly for commodities exhibiting higher volatility, will be essential for ensuring stable and efficient export performance over time.
