arXiv Machine Learning July 9, 2026 By Haoxin Liu, Yichen Zhou, Rajat Sen, B. Aditya Prakash, Abhimanyu Das

Rethinking Multimodal Time-Series Forecasting Evaluation

Read the original on arXiv Machine Learning →

arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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Overcoming the Modality Gap in Context-Aided Forecasting

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Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting

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TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting

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Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion

arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.

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Semantics-Enhanced Retrieval-Augmented Time Series Forecasting

arXiv:2606. 14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns.

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Overcoming the Modality Gap in Context-Aided Forecasting

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