LLM-ABBA: Understanding time series via symbolic approximation
This work addresses the challenge of effectively leveraging semantic information in time series for large language models (LLMs). To this end, we propose the first framework that deeply integrates adaptive Brownian bridge aggregation (ABBA) with LLMs. Methodologically: (1) we design an amplitude- and period-preserving ABBA symbolic representation to bridge temporal structure with LLM embedding spaces; (2) we introduce a fixed piecewise-linear chain reconstruction technique to significantly suppress cumulative quantization error; and (3) we combine fine-tuning, prompt engineering, and controllable symbolic–numerical inverse mapping to achieve semantic alignment. Our approach achieves state-of-the-art performance on UCR and three medical time-series classification benchmarks, as well as on the TSER regression benchmark—marking the first instance where an LLM surpasses prior methods on TSER. Moreover, its forecasting accuracy rivals that of advanced dedicated time-series models.