2026 June - 18 articles
Price spikes are rare but economically significant events observed across electricity, financial, commodity, and cryptocurrency markets. Their abrupt magnitude, heavy-tailed distributions, and severe class imbalance make them difficult to forecast us...
This study set out to investigate the interactions between business cycles, financial cycles and monetary policy in South Africa. Explicitly, the study aims to examine the role of financial factors in business cycle models and the possibility of a un...
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 lat...
The ski-jump spillway is an energy-dissipating structure that discharges extra water beyond the dam’s capacity. The scour process occurs below spillways due to the collision of the water jet with high energy. It is critical to acquire informati...
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...
South Africa remains one of the world’s most coal-dependent electricity systems, with coal accounting for 81.57% of generation in 2023. Despite policy interventions to diversify the energy mix, structural change is slow to emerge. This study pr...
This paper contributes to the stream of literature on extreme event modelling and forecasting by comparing various forecasting methods for predicting extreme movements in GDP and unemployment in the United States. The data were obtained from multiple...
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 investm...
Accurate long-horizon drought forecasting is essential for water resource management and early warning systems in semi-arid regions. This study evaluates five state-of-the-art Transformer architectures—Vanilla Transformer, Informer, Autoformer,...
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 a...
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 as...
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...
Predicting profitable entry signals in Bitcoin markets remains challenging due to price volatility, the absence of fundamental valuation frameworks, and methodological pitfalls that are common in the literature. In this study, we evaluate five machin...
AI/IoT initiatives are increasingly adopted in business, yet reported success varies substantially across firms. This study develops and evaluates a firm-level predictive framework for the reported AI/IoT success rate, measured on a bounded 0–1...
Singular Design Foresight (SDF) is proposed as a foundational methodological framework for advancing Design Foresight (DF) toward a more explicit, traceable, and evaluable scientific discipline. The framework formalizes DF as a structured cycle in wh...
Retail demand forecasting for fast-moving consumer goods (FMCGs) presents significant challenges due to high product variety, demand intermittency, and uncertainty, which prevent any single model from capturing the diverse demand patterns. To address...
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 econo...
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. Never...
