Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams

• URL: http://arxiv.org/abs/2112.03360v1
• Date: Mon, 6 Dec 2021 21:13:18 GMT
• Title: Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams
• Authors: Tahiya Chowdhury, Murtadha Aldeer, Shantanu Laghate, Jorge Ortiz
• Abstract summary: We show that our algorithm can robustly detect time-series events across different applications. We demonstrate its applicability in a real-world IoT deployment for ambient-sensing based activity recognition.
• Score: 1.2330326247154968
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Timeseries partitioning is an essential step in most machine-learning driven, sensor-based IoT applications. This paper introduces a sample-efficient, robust, time-series segmentation model and algorithm. We show that by learning a representation specifically with the segmentation objective based on maximum mean discrepancy (MMD), our algorithm can robustly detect time-series events across different applications. Our loss function allows us to infer whether consecutive sequences of samples are drawn from the same distribution (null hypothesis) and determines the change-point between pairs that reject the null hypothesis (i.e., come from different distributions). We demonstrate its applicability in a real-world IoT deployment for ambient-sensing based activity recognition. Moreover, while many works on change-point detection exist in the literature, our model is significantly simpler and matches or outperforms state-of-the-art methods. We can fully train our model in 9-93 seconds on average with little variation in hyperparameters for data across different applications.

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