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. 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. Index T erms-- Time series analysis, natural language generation, difference explanation, industrial IoT, large language model 1. INTRODUCTION The spread of the Internet of Things (IoT) has enabled large-scale collection of sensor data from industrial machinery.