Real-Time Change Point Detection with Application to Smart Home Time Series Data
Year: 2018
Type: article
Abstract: Change Point Detection (CPD) is the problem of discovering time points at which the behavior of a time series changes abruptly. In this paper, we present a novel real-time nonparametric change point detection algorithm called SEP, which uses Separation distance as a divergence measure to detect change points in high-dimensional time series. Through experiments on artificial and real-world datasets, we demonstrate the usefulness of the proposed method in comparison with existing methods.
Institution: Washington State University
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Awards: CPS: TTP Option: Synergy: Collaborative Research: The Science of Activity-Predictive Cyber-Physical Systems (APCPS), Smart Technologies for Health Assessment and Assistance, Multi-modal functional health assessment and intervention for individuals experiencing cognitive decline, Creating adaptive, wearable technologies to assess and intervene for individuals with ADRDs, A clinician-in-the-loop smart environment to support management of chronic health conditions
OpenAlex ID: W2811266412