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.
By Alexander Kreusel, Maximilian Beichter, Alexandra Nikoltchovska, Benjamin Sch\"afer, Ralf Mikut, Frederik Zahn, Veit Hagenmeyer
WhatIf-Bench is a semi‑synthetic benchmark that tests how well time‑series forecasters use covariates to predict a target’s response to future events. It generates 37,665 tasks from mechanistic simulators of data centers, power grids, road traffic, and water networks, varying covariate numbers, historical events, and event length. Evaluation shows large‑language models outperform the best time‑series foundation models, with all models performing well on replayed events but struggling on novel covariate settings, highlighting generalization as the main challenge.
By Arjun Ashok, Mihir Parmar, Palash Goyal, Bhavana Dalvi Mishra, Chun-Liang Li, Irina Rish, Alexandre Drouin, Jinsung Yoon, Tomas Pfister
arXiv:2610.07080v1 Announce Type: new
Abstract: Attention maps are often interpreted as evidence of what a forecasting model uses when making predictions. In our load-forecasting model, a CLS represe...
By Yuji Akamatsu, Takao Yamanaka
District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Conventional forecasting workflows train system-specific models on historical data, which can become burdensome when networks change through new consumers, retrofits, or changing operating regimes.
arXiv:2607. 19383v1 Announce Type: cross Abstract: Pretrained generative foundation models cast forecasting as conditional generation from a learned predictive distribution and forecast unseen series zero-shot.
By Ahmed Cherif
arXiv:2609.06656v1 Announce Type: cross
Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load foreca...
By Varsha Pendyala, Yiwei Fu, Weizhong Yan, Nurali Virani
arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair
arXiv:2609. 20193v1 Announce Type: new Abstract: Retrieval plug-ins supply a deep forecaster with information its lookback window cannot carry.
By Mert Onur Cakiroglu, Elham Buxton, Mehmet Dalkilic, Hasan Kurban
EnergyBench is a live, gate‑aligned benchmark that evaluates day‑ahead forecasts for European power markets every day at 11:30 CET/CEST, just before the SDAC gate closes. It runs models on a single, frozen information set—including three numerical weather prediction models—and publishes forecasts for price, wind, solar generation, and load across six bidding zones. Models are ranked using a Bradley‑Terry Elo system, with errors pooled against a seasonal‑naive baseline, allowing comparison of baselines, linear and gradient‑boosted models, foundation models, ensembles, and grid operators’ forecasts.
Why it matters: By evaluating models live on a fixed, publicly released information set, EnergyBench eliminates overfitting to static test sets and provides a realistic, deadline‑aligned assessment of forecasting performance across multiple market variables.
By Evgenii Genov, Ander Martinez Alonso, Christoph Bergmeir
arXiv:2609.39386v1 Announce Type: new
Abstract: Pretrained time-series foundation models (TSFMs) are evaluated as forecasters of future values, yet for sparse series many decisions depend only on whi...
By Daniel Schoess, Florian von Wangenheim
arXiv:2610.07232v1 Announce Type: new
Abstract: Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation...
By Tomas Kaljevic, Ivan Arzola, Yu Zhang
arXiv:2606. 28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting.
By Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar