Chronos-2 is a foundation model for multivariate time‑series forecasting that captures interactions among multiple series and heterogeneous covariates, enabling zero‑shot probabilistic forecasts without task‑specific tuning. It achieves state‑of‑the‑art accuracy, improving skill scores by 5.2 percentage points and win rates by 10 percentage points over leading baselines, while being computationally efficient—generating forecasts for 100 series in under one second on a single mid‑range GPU. The model demonstrates strong performance across diverse scientific domains such as infectious‑disease forecasting, macroeconomics, carbon‑flux prediction, and runoff simulation, matching or surpassing specialized methods.
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.
arXiv:2606. 27438v1 Announce Type: new Abstract: Since its initial release in 2020, Darts has become a widely used open-source Python library for time series analysis.
The paper introduces SimpleTimeBench, a diagnostic suite testing basic temporal primitives like monotonic trends, periodic signals, and leading indicator covariates. It finds that prominent multivariate Time Series Foundation Models (Chronos‑2, Moirai, and Toto) often produce suboptimal zero‑shot forecasts for these simple patterns, and that fine‑tuning can improve specific tasks while harming performance on other fundamentals. These failures persist in real‑world sensor forecasting, indicating that current TSFMs may lack the inductive biases needed to capture straightforward relationships, thereby limiting their practical reliability.
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...
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.
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:2608.20024v2 Announce Type: replace Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling. Forecasting models trained on historical da...
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.
arXiv:2606. 28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting.
Timer‑M1 is a pretrained multivariate time‑series foundation model that learns with primitives for zero‑shot forecasting. It uses a primitive‑based data synthesis and pretraining pipeline to generate series with shared temporal and relational patterns, assembling them into multivariate samples with distinct channel roles. The model employs gated two‑dimensional Transformer blocks that dynamically allocate cross‑variate attention, achieving top performance on several large‑scale forecasting benchmarks.
arXiv:2609.13345v1 Announce Type: cross Abstract: Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across...
arXiv:2607. 19659v1 Announce Type: new Abstract: Time-series foundation models can forecast across heterogeneous domains without task-specific training, but their forecasts are fixed once produced and cannot directly incorporate task-specific expert feedback.