Cadence: A Practical Time-series Partitioning Algorithm for Unlabeled IoT Sensor Streams
This provides a practical solution for IoT applications like activity recognition, though it is incremental as it builds on existing change-point detection methods.
The paper tackles the problem of time-series partitioning for unlabeled IoT sensor streams by introducing a sample-efficient, robust segmentation algorithm based on maximum mean discrepancy, which matches or outperforms existing change-point detection techniques on four datasets and can be trained in 9-93 seconds with little hyperparameter variation.
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 can be fully trained in 9-93 seconds on average with little variation in hyperparameters for data across different applications. We empirically evaluate Cadence on four popular change point detection (CPD) datasets where Cadence matches or outperforms existing CPD techniques.