arXiv:2606.26487v1 Announce Type: cross Abstract: Large language models (LLMs) are attractive for context-aware time series forecasting because they can integrate heterogeneous textual signals, yet their discrete, language-oriented tokenization and embedding interfaces are misaligned with continuous numerical values, often harming numerical ordering and forecasting reliability. We propose TempoWave, a plug-and-play temporal wavelet digit interface that maps each scalar observation into digit-wise embeddings constructed from multi-wavelet, multi-scale coefficients. By directly overriding standa
The rapid advancement and widespread adoption of large language models are exposing their limitations when handling continuous numerical data, such as in time series forecasting.
Improving LLMs' ability to process and interpret numerical data is crucial for their application in critical areas like finance, climate modeling, and predictive analytics, expanding their utility beyond purely linguistic tasks.
This research introduces a method to better integrate continuous numerical data into LLMs, potentially enhancing their accuracy and reliability in quantitative tasks that were previously challenging.
- · AI researchers
- · Time series forecasting software providers
- · Sectors reliant on predictive analytics
- · Developers of general-purpose LLMs
- · Traditional statistical forecasting models (if LLMs become more accurate)
- · Companies unable to adapt LLMs for numerical data
LLMs gain improved capabilities in processing and understanding numerical time-series data.
Enhanced LLM predictive accuracy could lead to more robust and reliable forecasting in various industries, from finance to supply chain management.
The integration of multi-modal data (text and numbers) into LLMs on a fundamental level could accelerate the development of more generalized and powerful AI agents.
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Read at arXiv cs.AI