arXiv AI
Benchmarking System One Models for Time-Series Decision Making: A Case Study of Jev
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FinVerse: Financial Time-Series Benchmark
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 AI
Jun 16
TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting
arXiv:2606. 16173v1 Announce Type: new Abstract: High-quality time series forecasting is pivotal for real-world decision-making.
arXiv AI
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FinVerse: Toward More Realistic Evaluation for Financial Time-Series Forecasting
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...
arXiv Statistics ML
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Foreclassing: From Forecasts to Decisions
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.
arXiv Machine Learning
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Forecast Skill Is Not Decision Skill: Evidence from Weather-Dependent Decision Tasks
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 Machine Learning
Jul 28
Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting
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.