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.
@article{arxiv.2509.20007,
title = {DiffNator: Generating Structured Explanations of Time-Series Differences},
author = {Kota Dohi and Tomoya Nishida and Harsh Purohit and Takashi Endo and Yohei Kawaguchi},
journal= {arXiv preprint arXiv:2509.20007},
year = {2025}
}