Author

B. Prakash

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#machine learning Preprint Oct 2026

TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models

Time-series foundation models provide strong zero-shot forecast, but deployment for real-world applications still requires task-specific designs for model inputs and outputs. Direct inference can leave domain knowledge and recurring forecast errors unaddressed, while tailored pipelines require substantial human experti...

Haoxin Liu, Deqing Fu, Lu-Cheng Fu et al. · 0 citations

Jul 2026

Rethinking Multimodal Time-Series Forecasting Evaluation

A thorough empirical study of zero-shot multimodal forecasting approaches on TimesX suggests that many approaches that perform well on existing benchmarks may fail on TimesX, and simple ensemble methods that leverage rich textual context accompanying time-series can outperform strong baselines on TimesX.

Haoxin Liu, Yi-Chen Zhou, Rajat Sen et al. · arXiv.org · 7 citations