arXiv:2602.03164v2 Announce Type: replace Abstract: Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications. Recently, large language model (LLM)- based forecasters have made promising advancements. Despite their effectiveness, existing methods often lack explicit experience accumulation and continual evolution. In this work, we propose MemCast, a learning-to-memory framework that reformulates TSF as an experience-conditioned reasoning task. Specifically, we learn experience from the training set and organize it into a hierarchical memory. This i
The proliferation of Large Language Models (LLMs) in various domains is driving continuous research into enhancing their capabilities for critical applications like time series forecasting.
This development proposes a novel approach to time series forecasting, moving beyond current LLM limitations by integrating explicit experience accumulation and continual learning, which can significantly improve decision-making accuracy in real-world scenarios.
The explicit incorporation of 'experience-conditioned reasoning' into forecasting models marks a paradigm shift from purely data-driven or LLM-based approaches, potentially leading to more robust and adaptive predictions.
- · AI researchers
- · Data scientists
- · Financial institutions
- · Logistics and supply chain companies
- · Companies reliant on less sophisticated forecasting models
Improved accuracy and adaptability of time series forecasting for a wide range of applications.
Reduced errors in planning and resource allocation across industries, leading to efficiency gains.
Accelerated development of autonomous decision-making systems that can learn and adapt from past experiences more effectively.
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