TSLM-ICL: Toward Effective In-Context Learning for Time Series Language Models

Time series language models (TSLMs) combine temporal signals with natural-language information, enabling tasks such as perception, reasoning, and decision making. In-context learning (ICL) offers a training-free way to adapt frozen TSLMs through demonstrations, yet existing time-series ICL methods are largely task-specific and do not address the heterogeneous structure of multimodal time-series data. We introduce **TSLM-ICL**, a training-free, model-agnostic retrieval framework for multimodal demonstration selection. TSLM-ICL represents samples using token-level temporal and textual embeddings, compares variable-sized representations via optimal transport, and selects relevant demonstrations under a fixed context budget. Across five public multimodal time-series benchmarks and six frozen TSLMs, TSLM-ICL improves accuracy by up to 16% over zero-shot prompting and consistently outperforms random, text-only, TimeRAG, and TS-RAG retrieval, achieving the best average accuracy in 14 of 15 dataset–budget configurations.