arXiv:2606.08262v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened new possibilities for time series forecasting by enabling alignment between temporal patterns and pretrained word embeddings. However, most LLM-based methods overlook the heterogeneous nature of time series, where dynamic fluctuations and invariant semantics are entangled. This entanglement introduces spurious correlations during the alignment, as dynamic components act as confounders by simultaneously influencing invariant components and the resulting aligned embeddings. To address this
The rapid advancement of LLMs has exposed current limitations in applying them to complex, dynamic data like time series, creating an urgent need for more sophisticated alignment techniques.
Improving LLM-based time series forecasting can unlock new levels of predictive accuracy for critical applications across finance, logistics, and resource management.
This research outlines a method to better integrate LLMs with time series data by addressing the entanglement of dynamic fluctuations and invariant semantic components.
- · LLM developers
- · Data scientists
- · Industries relying on time series forecasting
- · Financial modeling
- · Traditional time series models
- · Methods overlooking data heterogeneity
More accurate and robust forecasting models will emerge, driven by causally informed LLM alignments.
Enhanced predictive capabilities will enable more efficient resource allocation and risk management across various sectors.
The broader adoption of these advanced forecasting methods could lead to new financial products and operational efficiencies at scale.
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Read at arXiv cs.LG