arXiv:2606.08601v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently demonstrated impressive potential for time series forecasting. However, existing methods predominantly rely on passive modality alignment or static task reprogramming, which often fail to capture fine-grained, non-stationary temporal patterns or to adapt to nuanced task intents. In this paper, we propose Instruction-aware Active Probing (InA-Probe), which shifts the paradigm from passive alignment toward an active, instruction-driven probing mechanism. Specifically, we design a Multi-Level Instruction In
The rapid advancement and widespread application of Large Language Models (LLMs) across various domains necessitate improved methods for specialized tasks like time series forecasting.
This development could significantly enhance the accuracy and adaptability of LLMs in finance, logistics, and other industries reliant on precise temporal predictions.
The paradigm shifts from passive modality alignment to an active, instruction-driven probing, allowing LLMs to better capture nuanced, non-stationary temporal patterns.
- · AI researchers
- · Companies using time series forecasting
- · Cloud AI providers
- · Traditional time series forecasting methods
- · Developers relying on static LLM integrations
More accurate and dynamic time series predictions leveraging LLMs become standard.
New AI-powered tools emerge that can adapt to rapidly changing market conditions or complex industrial processes with greater precision.
LLM-driven automation extends into highly complex, non-linear systems previously deemed too volatile for AI management.
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Read at arXiv cs.AI