arXiv:2506.17631v4 Announce Type: replace Abstract: Time series forecasting aims to model temporal dependencies among variables for future state inference, holding significant importance and widespread applications in real-world scenarios. Although deep learning-based methods have achieved remarkable progress, they still exhibit suboptimal performance in long-term forecasting. Recent research demonstrates that large language models (LLMs) achieve promising performance in time series forecasting, but this progress is still met with skepticism about whether LLMs are truly useful for this task. T
Ongoing advancements in LLMs and deep learning are pushing their applicability into diverse domains, and time series forecasting is a critical area for real-world applications.
Improving long-term time series forecasting with LLMs could significantly enhance decision-making across industries, from finance to logistics and scientific research.
The perceived utility and effectiveness of LLMs for complex, quantitative tasks like time series forecasting are being re-evaluated, potentially expanding their functional scope beyond natural language.
- · AI developers
- · Data scientists
- · Industries relying on forecasting
- · LLM platforms
- · Traditional forecasting model developers
- · Companies slow to adopt advanced AI tools
Further research and development will focus on integrating LLMs into specialized forecasting applications.
Increased reliance on LLM-powered forecasting could lead to new challenges in model interpretability and bias detection.
LLMs could become foundational components for autonomous decision-making systems in highly dynamic environments.
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Read at arXiv cs.LG