Forecasting

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.

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.

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.