DiffNator: Generating Structured Explanations of Time-Series Differences
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908556686524416 |
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| author | Dohi, Kota Nishida, Tomoya Purohit, Harsh Endo, Takashi Kawaguchi, Yohei |
| author_facet | Dohi, Kota Nishida, Tomoya Purohit, Harsh Endo, Takashi Kawaguchi, Yohei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffNator: Generating Structured Explanations of Time-Series Differences Dohi, Kota Nishida, Tomoya Purohit, Harsh Endo, Takashi Kawaguchi, Yohei Computation and Language 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. |
| title | DiffNator: Generating Structured Explanations of Time-Series Differences |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.20007 |