RideBench is a new benchmark built on a large-scale ride‑hailing time‑series dataset called Ride‑Hailing, which spans four years of half‑hourly data across 200 spatial areas and includes three exogenous scenarios: weather disturbance, holiday effect, and large‑scale event impact. The benchmark evaluates over 30 forecasting methods for both week‑ahead and 8‑week‑ahead horizons, revealing that while future‑known exogenous variables improve week‑ahead accuracy, current models still struggle with disturbance‑induced pattern changes and long‑horizon forecasting. These results highlight a mismatch between existing models and real‑world ride‑hailing needs, underscoring the necessity for better exploitation of exogenous information, scalability, and long‑horizon planning capabilities.
arXiv:2610.31959v1 Announce Type: cross Abstract: Short-term building load forecasting remains challenging because load patterns can change rapidly with weather and operating conditions. Data-driven...
arXiv:2603. 15802v2 Announce Type: replace Abstract: In many time series forecasting settings, the target time series is accompanied by exogenous covariates, such as promotions and prices in retail demand; temperature in energy load; calendar and holiday indicators for traffic or sales; and grid load or fuel costs in electricity pricing.
The paper investigates how different calendar feature encodings influence the performance of covariate-informed time series foundation models (TSFMs). Seven encoding schemes—three target‑agnostic (ordinal, one‑hot, cyclic) and four target‑based—are systematically compared across four state‑of‑the‑art TSFMs on nine datasets that reflect social activity patterns such as weekends and holidays. Results show that encoding choice can change forecast accuracy by up to 9.9 % on non‑holidays and 83 % on holidays, with improvements mainly around holidays and varying effects across models.
arXiv:2607. 14871v1 Announce Type: cross Abstract: In many operational time-series forecasting applications, such as crowd demand forecasting, the risk related to under-prediction is substantially higher than that of over-prediction.
arXiv:2608. 00402v1 Announce Type: new Abstract: Estimated Time of Arrival (ETA) prediction is a core component of intelligent transportation systems.
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.
arXiv:2607. 13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions.
arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.
The paper investigates how post‑generation assembly of fixed‑length synthetic windows affects downstream forecasting performance. By varying overlap rates and window‑weighting schemes, the authors evaluate boundary continuity, statistical and temporal fidelity, and TSTR utility across four public datasets and five forecasting models. Results show that optimal assembly depends on the forecaster, with increased overlap improving continuity but not always reducing forecasting error, and provide assembly grids and TRTR references to quantify these effects.
arXiv:2608. 04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows.
arXiv:2604. 26634v2 Announce Type: replace Abstract: Norway's electricity market is heavily dominated by hydropower, but the 2021-2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models calibrated on historical data.
arXiv:2610.14863v1 Announce Type: new Abstract: Data-driven weather forecasting models are commonly trained on ERA5, yet operational deployment must use timely initial states from physics-based numer...