The paper presents a machine‑learning approach to predict train delays in Finland using the Finland Integrated Train‑Weather (FI‑TW) dataset, which merges railway operational records with data from about 200 nationwide weather sensors. Three feature sets were tested with XGBoost at Oulu central station: full weather features, instant observations only, and derived weather categories. The category‑based features—hierarchical classes such as Blizzard, Heavy Snow, and Extreme Cold—yielded the best performance, achieving an R² of 0.78, RMSE of 8.5 min, and MAE of 3.7 min, an 11 % R² improvement and 10 % error reduction over the other configurations.
arXiv:2601. 16592v2 Announce Type: replace-cross Abstract: Train delays result from complex interactions between operational, technical, and environmental factors.
arXiv:2606. 05070v1 Announce Type: new Abstract: Train delay prediction is an important problem for both passengers and railway operators, yet progress in the field remains difficult to assess due to the lack of standardized datasets, prediction targets, and evaluation protocols.
The study evaluates three machine‑learning weather‑prediction models—FourCastNet3, GraphCast, and the ECMWF High‑Resolution Forecast—for wind‑speed forecasting in Northern Norway using multi‑year station data. Results show the ECMWF model slightly outperforms the ML models (RMSE 2.89 m s⁻¹ vs. 2.96 m s⁻¹ for FourCastNet3 and 2.94 m s⁻¹ for GraphCast), yet all models maintain comparable performance beyond their training periods and underestimate strong winds. FourCastNet3 performs best under high‑wind conditions, indicating ML models are competitive with traditional numerical weather prediction but still need improvement for complex terrain.
arXiv:2608. 11446v1 Announce Type: new Abstract: This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (DHS).
This paper presents a comparative study of two distinct approaches, XGBoost and Long-Short Term Memory (LSTM), for forecasting transmitted heat energy in District Heating Systems (DHS). The objective is to explore scenarios in which conventional ML algorithms demonstrate better performance over deep learning networks in time series forecasting and the associated benefits in terms of computational cost and environmental impact.
The paper introduces RSTGCN, a Railway-centric Spatio-Temporal Graph Convolutional Network that predicts average arrival delays for all incoming trains at a specific station during a given time period. It incorporates train frequency-aware spatial attention and other architectural innovations to improve predictive accuracy. Experiments on a newly released dataset covering 4,735 Indian Railway stations show RSTGCN outperforms state‑of‑the‑art baselines by 18% in MAE, 14% in MAPE, and 1–8% in RMSE.
arXiv:2609.07512v1 Announce Type: new Abstract: Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challe...
arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge.
arXiv:2607. 28220v1 Announce Type: cross Abstract: Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states.
arXiv:2607. 05658v1 Announce Type: cross Abstract: Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals.
arXiv:2508. 18486v2 Announce Type: replace-cross Abstract: Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP).