From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization
arXiv:2508.09191v2 Announce Type: replace-cross Abstract: Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limited due to the challenge of integrating historical numerical sequences with contextual features, which often comprise unstructured textual data. To address this challenge, we propose TokenCast, a large language model (LLM) driven framework that leverages language-based symbolic representations as a unified intermediary f
The proliferation of advanced LLMs and the increasing demand for robust decision-making in complex systems drive the need for better integration of structured and unstructured data in forecasting.
This development enhances the accuracy and context-awareness of time series forecasting, which is critical for decision-making across finance, energy, and healthcare sectors.
The ability to seamlessly integrate contextual textual data with numerical time series through symbolic discretization and LLMs significantly improves forecasting capabilities, moving beyond traditional statistical models.
- · AI/ML developers
- · Financial institutions
- · Healthcare providers
- · Energy sector companies
- · Traditional time series forecasting models
- · Companies relying solely on numerical data for decisions
Improved predictive analytics lead to more efficient resource allocation and risk management in various industries.
The fusion of LLMs with numerical forecasting could accelerate the development of more sophisticated AI agents capable of autonomous decision-making.
Enhanced forecasting across critical sectors may lead to greater economic stability and more resilient global supply chains through better anticipation of disruptions.
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Read at arXiv cs.AI