time-series-foundation-models-7a390e08·2 events·first seen Aliases: Time Series Foundation Models
Researchers introduce RMISC, an openly accessible corpus of ~200 datasets and 142 billion time points of real-world multivariate time series data, designed to address the gap between synthetic and real-world pretraining data for time series foundation models (TSFMs). Four advanced TSFMs are pretrained on univariate, synthetic multivariate, and real-world multivariate data and evaluated on zero-shot generalization benchmarks. Results show that real-world multivariate pretraining data consistently improves generalization for both univariate and multivariate TSFMs. The work provides both a reusable dataset resource and empirical evidence on the synthetic-vs-real data question for time series modeling.
This paper introduces a 'last-mile forecasting' framework where an LLM agent sits atop a statistical forecasting backbone to incorporate weakly structured business context—holidays, campaigns, expert feedback, external events—into decision-ready forecasts. The system uses tool-invocation for contextual retrieval, converts reasoning into explicit revision actions under safety constraints, and supports long-horizon forecasting via map-reduce decomposition with a memory bank for post-hoc reflection. The authors validate the approach through real-world case studies, positioning it as a bridge between statistical prediction and operationally usable forecasts.