arXiv AI
JudgeCast: Time Series Forecasting with Experience-Informed Covariate Judgements
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jun 10
CITRAS: Covariate-Informed Transformer for Time Series Forecasting
arXiv:2503. 24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables.
arXiv Machine Learning
Sep 22
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
arXiv:2609.24862v1 Announce Type: new Abstract: Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning s...
arXiv AI
Jun 4
Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty
arXiv:2606. 04342v1 Announce Type: cross Abstract: Multi-step time series forecasting (MSF) is commonly evaluated using point-wise error metrics such as mean squared error (MSE), implicitly treating the conditional mean as a sufficient target.
arXiv Machine Learning
Jul 23
Expert-Guided Forecast Editing for Time-Series Foundation Models
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.
arXiv AI
Sep 4
RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.
Hugging Face Trending Papers
Sep 21
When Tomorrow Becomes Today: Self-Evolving Policies for Agentic Time-Series Forecasting
Agentic time series forecasting concerns systems whose underlying mechanisms evolve, making the relative effectiveness of numerical models, reasoning strategies, and intervention rules inherently time...