The automatic generation of representative natural language descriptions for observable patterns in time series data enhances interpretability, simplifies analysis and increases cross-domain utility of temporal data. While pre-trained foundation models have made considerable progress in natural language processing (NLP) and computer vision (CV), their application to time series analysis has been hindered by data scarcity. Although several large language model (LLM)-based methods have been proposed for time series forecasting, time series captioning is under-explored in the context of LLMs. In this paper, we introduce TSLM, a novel time series language model designed specifically for time series captioning. TSLM operates as an encoder-decoder model, leveraging both text prompts and time series data representations to capture subtle temporal patterns across multiple phases and generate precise textual descriptions of time series inputs. TSLM addresses the data scarcity problem in time series captioning by first leveraging an in-context prompting synthetic data generation, and second denoising the generated data via a novel cross-modal dense retrieval scoring applied to time series-caption pairs. Experimental findings on various time series captioning datasets demonstrate that TSLM outperforms existing state-of-the-art approaches from multiple data modalities by a significant margin.

The recent emergence of Large Language Models (LLMs) has reshaped the landscape of natural language processing, ushering in a new era of computational linguistics. Bolstered by advancements in machine learning and deep neural networks, LLMs have garnered widespread attention for their remarkable ability to understand and generate human-like text. This transformative technology has revolutionized various applications, ranging from machine translation and sentiment analysis to chatbots and content generation. By leveraging vast amounts of text data and sophisticated algorithms, LLMs have demonstrated unparalleled proficiency in capturing linguistic nuances, contextual dependencies, and semantic meanings.