Title: OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data
Primary area: foundation or frontier models, including LLMs
Keywords: Time Series Language Models, TSLM, Digital Health, Time series, Wearables, Sensors, Reasoning, Chain of Thought, Medicine

Abstract: Large Language Models have emerged as powerful tools for interpreting multimodal data (e.g., images, audio, text), often surpassing specialized models. In medicine, they hold particular promise for synthesizing large volumes of clinical information into actionable insights and patient-facing digital health applications. Yet, a major limitation remains their inability to handle time series data. To overcome this gap, we present OpenTSLM, a family of Time Series Language Models created by integrating time series as a native modality to pretrained LLMs, enabling natural-language prompting and reasoning over multiple time series of any length. We investigate two architectures that differ in how they model time series. The first, OpenTSLM-SoftPrompt, models time series implicitly by concatenating learnable time series tokens with text tokens via soft prompting. Although parameter-efficient, we hypothesize that explicit time series modeling scales better and outperforms implicit approaches. We thus introduce OpenTSLM-Flamingo, which integrates time series with text via cross-attention. We benchmark both variants with LLaMa and Gemma backbones against baselines that treat time series as text tokens or plots, across a suite of text–time-series reasoning tasks. We introduce three time-series Chain-of-Thought (CoT) datasets: HAR-CoT (human activity recognition), Sleep-CoT (sleep staging), and ECG-QA-CoT (ECG question answering). Across all, OpenTSLM models consistently outperform baselines, reaching 69.9% F1 in sleep staging and 65.4% in HAR, compared to 9.05% and 52.2% for finetuned text-only models. Notably, even 1B-parameter OpenTSLM models surpass GPT-4o (15.47% and 2.95%). OpenTSLM-Flamingo matches OpenTSLM-SoftPrompt in performance and outperforms on longer sequences, while maintaining stable memory requirements. By contrast, SoftPrompt exhibits exponential memory growth with sequence length, requiring ~110 GB compared to ~40 GB VRAM when training on ECG-QA with LLaMA-3B. Expert reviews by clinicians find strong reasoning capabilities and temporal understanding of raw sensor data exhibited by OpenTSLMs on ECG-QA. To facilitate further research, we provide all code, datasets, and models open-source.

TL;DR: We introduce OpenTSLM, an open-source framework and family of time series language models that natively include raw time series data, support arbitrary-length inputs, evaluated on chain-of-thought reasoning tasks, strongly outperforming baselines.