14 November 2026
Long-horizon peak-demand projections combine statistical uncertainty with assumptions about electrification and new connections. This study separates those sources of variation. A statistical baseline combines long short-term memory, LightGBM and tem...
Ground-based millimeter and submillimeter astronomy requires detailed astroweather and astroclimatic studies of potential sites in mountain regions. Using the mesoscale WRF model, validated against data from three upper-air stations, we examined the...
This study asks whether physical climate-risk transmission across insurance, reinsurance, climate-vulnerable sovereign bond, real-estate, agricultural, water, and adaptation markets is detectable in daily data. A news-based physical-risk index and se...
Built-in attention is often presented as a free explanation of what a forecasting transformer relies on, but the claim is rarely tested. We test it on MeteoFormer, a patch-based transformer with single-head variable and temporal attention that foreca...
Air quality indices, energy load levels, and similar series are recorded as ordered categories, yet standard forecasting methods produce continuous predictions. We propose a two-stage approach using NeuralProphet, which decomposes a series into trend...
Whether tourism has recovered from the COVID-19 shock depends on the benchmark used. This study combines interrupted time-series models, structural-break detection, an event-study specification and counterfactual forecasts to quantify the shock and r...
This study investigates whether U.S. macro-financial indicators improve one-step-ahead forecasts of return connectedness among gold, Brent crude oil, and the U.S. Dollar Index beyond autoregressive persistence. We estimate a time-varying Total Connec...
Regional day-ahead wind power forecasting (WPF) requires spatial dependence to adapt to forecast weather. Many existing graph models mainly capture pairwise relations derived from static geography or historical correlations, limiting their representa...
Wind forecasting in complex coastal terrain, such as the Colombian Caribbean, is hindered by air–sea heat fluxes and nonlinear atmospheric dynamics. This pre-registered systematic review and meta-analysis tests whether advanced architectures (a...
Satellite Earth observation (EO) is widely proposed as alternative data for agricultural credit risk, yet reported fusion gains are rarely tested against the protocol producing them. We specify three inexpensive instruments—matched alert volume...
Accurate liquefied petroleum gas (LPG) price forecasts are relevant to procurement and inventory planning, yet regional differences within China have received limited attention. This empirical study benchmarks 16 learned time-series models and a Naiv...
Financial forecasting under uncertainty is fundamental to strategic investment decision making. Among the factors that influence investment decisions, market efficiency plays a critical role because it determines whether historical information can pr...
Estimating paid parking demand is difficult when transaction data are censored by full occupancy and when tariff changes create structural breaks. This study proposes an interpretable three-level framework combining (i) dynamic modelling of the cumul...
Operational apparel forecasting requires comparisons that combine strong classical baselines, modern learning architectures, sensitivity analysis, and evaluation across multiple forecast origins. This study benchmarks nine methods for one-step-ahead...
Reliable forecasts of GDP growth and price dynamics support macroeconomic monitoring and economic planning, particularly in heterogeneous African economies where macroeconomic relationships may vary across economic conditions. This study develops and...
This study describes a validation of the surface wind model WindNinja conducted over different types of complex terrain. The Weather Research and Forecasting model (WRF) 2 km output was used as input for our WindNinja simulations. We tested the surfa...
Accurate long-term wind-power forecasting benefits from probabilistic scenario generation that reflects both the temporal dependence of wind conditions and the uncertainty associated with distributional parameters. This study develops a hybrid Statis...
Many studies report forecasting gains for sophisticated machine-learning and deep-learning models without testing whether those gains survive comparison against simple, reproducible benchmarks. This study tests that question for one-step-ahead monthl...
Atmospheric visibility is an important meteorological variable affecting transportation, tourism, and agricultural production. Guizhou Province, located in the eastern Yunnan-Guizhou Plateau, has complex terrain and high humidity and is prone to fog-...
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 mod...
Many economic time series are compositions: educational attainment shares, waste treatment routes, energy mixes, and sectoral employment. Forecasters usually move such series to log-ratio coordinates and apply generic methods that impose no economic...
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 operat...
Geopolitical uncertainty may affect financial markets, but its incremental value for forecasting emerging-market stock returns remains unclear. Using monthly data from January 1990 to July 2026, this study compares ARIMA-GARCH and ARIMAX-GARCH benchm...
Forecasting quarterly revenue during structural breaks remains challenging, particularly when only limited historical data are available and interpretability is required for business decision-making. This study proposes an interpretable forecasting f...
Demand variability complicates workforce capacity planning in knowledge-intensive services, particularly when only short historical records are available. This study evaluates parsimonious benchmark models for forecasting translation-service demand a...
Accurate stock price forecasting remains a challenging problem because financial markets are highly dynamic, nonlinear, and influenced by changing economic conditions. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, have sh...
This study proposes a Generalized Bayesian Structural Time Series (GBSTS) framework for forecasting seasonal time series with sparse observations. Rather than introducing new state-space components, the proposed framework systematically integrates al...
This study audits whether regime weighting can sharpen conformal forecast intervals without using target-period information or omitting the required finite-sample correction. Conformal prediction builds such intervals from past forecast errors. A nat...
Pension funds and insurers in emerging markets size long-horizon capital with economic scenario generators (ESGs)—econometrically, simulators of a multivariate predictive density. The production benchmark draws its innovations by Gaussian Chole...
Forecasting
Accurate wind power forecasting is essential for the stable and economic operation of power systems with high renewable penetration. Although machine learning models have been widely adopted for this task, the assumption that greater model complexity...
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 mode...
Online conformal prediction methods such as Adaptive Conformal Inference (ACI) and Fully Adaptive Conformal Inference (FACI) adjust prediction intervals under distribution shift, but their calibration is based on a common stream of recent nonconformi...
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...
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 2...
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...
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 pric...
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...
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 benchma...
Accurate Estimated Time of Arrival (ETA) forecasting is essential for improving operational planning and decision-making in modern ports. While machine learning has significantly enhanced ETA prediction using Automatic Identification System data, the...
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 patte...
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 t...
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...
High-performance computing based on general-purpose graphical processing units (GPUs) is a powerful tool for reducing computational time. In a context where big data is becoming increasingly relevant, GPUs may play a crucial role. This study analyzes...
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...
The growing integration of variable renewable energy sources (VRES) challenges power system flexibility and may cause curtailment due to excess capacity, grid constraints, or operational and market factors. Power-to-Heat (PtH) technology can mitigate...
As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in parts of th...
Reliable power systems are essential to modern life, as severe storms continue to disrupt grid stability and cause widespread outages. Predicting storm outages enables utilities and emergency managers to pre-stage resources and improve resilience. Ho...
Accurate electricity price forecasting is essential for market participants seeking to optimise bidding and arbitrage strategies. This paper presents a week-ahead (168 h) hourly electricity price forecasting study for the Spanish day-ahead market. Ni...
Short-term rental housing plays an important role in the housing market by increasing property utilization and generating income opportunities for property owners. This study investigates the key attributes associated with Airbnb occupancy rates usin...
