The autonomous identification of time-steps where the behavior of a time-series significantly deviates from a predefined model, or time-series change point detection, is an active field of research with notable applications in finance, health, and advertising. One family of time-series change detection algorithms, referred to as "model-based methods", although useful for many applications, performs poor when the data are noisy and have outliers. We introduce a new framework that enables existing model-based methods to be more robust to these data challenges. We demonstrate the effectiveness of our approach on remote sensing and mobile health data. Our method introduces two new concepts: (i) a random sampling procedure allows us to overcome outliers, and (ii) a matrix-based representation of anomaly scores provides a flexible and intuitive way to identify multiple types of changes and test their significance. We show that our method performs better than several baseline methods, including application-specific algorithms, and provide all data and open-source code.
|Original language||English (US)|
|Title of host publication||16th SIAM International Conference on Data Mining 2016, SDM 2016|
|Editors||Sanjay Chawla Venkatasubramanian, Wagner Meira|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2016|
|Event||16th SIAM International Conference on Data Mining 2016, SDM 2016 - Miami, United States|
Duration: May 5 2016 → May 7 2016
|Name||16th SIAM International Conference on Data Mining 2016, SDM 2016|
|Other||16th SIAM International Conference on Data Mining 2016, SDM 2016|
|Period||5/5/16 → 5/7/16|
Bibliographical noteFunding Information:
This research was supported in part by the National Science Foundation Awards 1029711, 0905581, 1555949, and 1464297. The NASA Award NNX12AP37G and an UMII MnDRIVE Fellowship. Access to computing facilities was provided by the University of Minnesota Supercomputing Institute.
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