arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.
arXiv:2606. 16173v1 Announce Type: new Abstract: High-quality time series forecasting is pivotal for real-world decision-making.
arXiv:2608.03259v3 Announce Type: replace-cross Abstract: Time-series foundation models (TSFMs) are increasingly evaluated on broad benchmarks that apply uniform conventional forecast evaluation metr...
The paper introduces Foreclassing, a new machine learning problem where the class label of a time series is defined by its future observations. It provides a mathematical formulation, theoretical results on how future information influences Bayes risk and classification error, and proposes ForeClassNet, a neural network that jointly forecasts and classifies while accounting for uncertainty. Experiments across weather, energy, and finance datasets show that Foreclassing can improve classification accuracy, probabilistic predictions, and downstream decision-making compared to conventional methods.
The paper argues that traditional weather forecast evaluations, which focus on statistical comparisons between forecasts and observations, do not adequately capture how forecasts influence real-world decisions. It introduces decision calibration, a framework that assesses probabilistic forecast performance from the decision-maker’s perspective. Using this framework, the authors compare a machine learning model to a classical numerical weather prediction model across various weather-dependent decision tasks, finding that forecast-level performance does not reliably predict decision-level outcomes and that model rankings can shift depending on the decision context.
arXiv:2607. 23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never seen during pre-training.
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...
arXiv:2601. 23204v2 Announce Type: replace Abstract: Time series data are integral to critical applications across domains such as finance, healthcare, transportation, and environmental science.
arXiv:2509. 26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models.
arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...
arXiv:2608. 14054v1 Announce Type: new Abstract: Time series forecasting with pretrained foundation models has demonstrated strong zero-shot capabilities.
arXiv:2602. 12147v4 Announce Type: replace Abstract: Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.