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arXiv Machine Learning By Jierui Lei, Wenjian Zhang, Qingyi Yang, Yuyang Hong, Fangzheng Chen, Zhengbo Zhang, Haina Tang, Shiming Xiang

Revisiting Identity and Spectra Dispersion in Media-Bridged Time Series Forecasting: Linking Multivariate Signals and Narrative Flows

Read the original on arXiv Machine Learning →

The paper introduces MIDAPN, a unified spatiotemporal forecasting backbone that integrates media-general graph adaptation and automatic temporal learning for both multivariate and multimodal time series. It employs a Multimedia Identity-Aware Graph to capture static, dynamic, and latent relationships across media, and a Spectral Prism Convolution for hierarchical temporal analysis. Extensive experiments on 13 multivariate and 12 multimodal datasets show MIDAPN consistently outperforms 16 state‑of‑the‑art TSF models and is compatible with various foundation models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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