arXiv:2605.30002v1 Announce Type: new Abstract: Cross-domain multimodal time series forecasting is a challenging task, requiring models to integrate precise numerical comprehension, cross-domain semantic understanding, and effective multimodal fusion. Existing approaches either build Time Series Foundation Models (TSFMs) from scratch or leverage pretrained Large Language Models (LLMs). However, TSFMs often overlook semantic understanding and lack the ability to perform future-oriented semantic reasoning, and LLMs struggle with numerical comprehension and accurate quantitative forecasting. To o
The accelerating development of both Time Series Foundation Models (TSFMs) and Large Language Models (LLMs) has highlighted their respective strengths and weaknesses in complex forecasting tasks, necessitating novel fusion approaches.
This development represents a significant step towards more sophisticated and reliable forecasting, crucial for decision-making across various industries and strategic planning.
The ability to combine precise numerical comprehension with cross-domain semantic understanding in time series forecasting creates more robust and context-aware predictions, potentially leading to better operational and strategic outcomes.
- · AI developers
- · Financial institutions
- · Logistics and supply chain management
- · Data scientists
- · Traditional forecasting models
- · Companies relying solely on siloed data analysis
Improved accuracy and contextual richness in time series predictions across diverse applications.
Increased automation of analytical tasks and strategic planning cycles in industries leveraging such advanced forecasting.
New competitive advantages for businesses capable of integrating and leveraging these advanced agentic forecasting systems effectively.
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Read at arXiv cs.AI