arXiv:2602.01588v3 Announce Type: replace Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals. The core challenge is to effectively combine temporal numerical patterns with the context embedded in other modalities, such as text. While most existing methods align textual features with time-series patterns one step at a time, they neglect the multiscale temporal influences of contextual information such as time-series cycles and dynamic shifts. This mismatch between local alignment and global tex
The paper addresses a core limitation in current multimodal time-series forecasting, which is critical as AI models are increasingly deployed in real-world scenarios requiring complex contextual understanding.
This research provides a more sophisticated method for integrating textual context with numerical time series, enhancing the accuracy and robustness of forecasting in applications ranging from finance to climate modeling.
Existing multimodal forecasting methods often neglect multiscale temporal influences of contextual information, but this new frequency-aware approach allows for better capture of time-series cycles and dynamic shifts.
- · AI/ML researchers
- · Data scientists
- · Financial institutions (algorithmic trading)
- · Supply chain management
- · Companies relying on less sophisticated forecasting models
- · Traditional statistical forecasting methods
Improved accuracy in predictive models that rely on both numerical data and textual context.
Faster and more reliable decision-making in sectors heavily dependent on forecasting, such as logistics, finance, and climate science.
Enhanced automation of complex analytical tasks currently requiring human interpretation of diverse data streams, potentially impacting white-collar workflows.
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Read at arXiv cs.LG