{"ID":2866528,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.20007","arxiv_id":"2509.20007","title":"DiffNator: Generating Structured Explanations of Time-Series Differences","abstract":"In many IoT applications, the central interest lies not in individual sensor signals but in their differences, yet interpreting such differences requires expert knowledge. We propose DiffNator, a framework for structured explanations of differences between two time series. We first design a JSON schema that captures the essential properties of such differences. Using the Time-series Observations of Real-world IoT (TORI) dataset, we generate paired sequences and train a model that combine a time-series encoder with a frozen LLM to output JSON-formatted explanations. Experimental results show that DiffNator generates accurate difference explanations and substantially outperforms both a visual question answering (VQA) baseline and a retrieval method using a pre-trained time-series encoder.","short_abstract":"In many IoT applications, the central interest lies not in individual sensor signals but in their differences, yet interpreting such differences requires expert knowledge. We propose DiffNator, a framework for structured explanations of differences between two time series. We first design a JSON schema that captures th...","url_abs":"https://arxiv.org/abs/2509.20007","url_pdf":"https://arxiv.org/pdf/2509.20007v1","authors":"[\"Kota Dohi\",\"Tomoya Nishida\",\"Harsh Purohit\",\"Takashi Endo\",\"Yohei Kawaguchi\"]","published":"2025-09-24T11:27:07Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}