Unsupervised Online Memory-free Change-point Detection using an Ensemble of LSTM-Autoencoder-based Neural Networks (Extended Abstract)

Zahra Atashgahi*, Decebal Constantin Mocanu, Raymond N.J. Veldhuis, Mykola Pechenizkiy

*Corresponding author for this work

Research output: Contribution to conferencePaperAcademicpeer-review

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Abstract

Change-point detection (CPD) is recognized as being one of the most significant tasks in time series analysis. While offline CPD has been vastly investigated in the last few years, online CPD still suffers from major challenges, such as high dependency on the choice of hyperparameters and prior information about data. However, in most real-world applications, very little prior information about the data is available, and performing hyperparameter tuning might not be feasible. Our proposed method, Adaptive LSTM-Autoencoder Change-point Detection (ALACPD), aims to address these challenges by performing unsupervised online memory-free CPD and continuously adapting itself to the current behavior of the system
Original languageEnglish
Publication statusAccepted/In press - Sep 2021
Event8th ACM Celebration of Women in Computing womENcourage - virtual (coordinated from Prague, Czech Republic), Prague, Czech Republic
Duration: 22 Sep 202124 Sep 2021
https://womencourage.acm.org/2021/

Conference

Conference8th ACM Celebration of Women in Computing womENcourage
Country/TerritoryCzech Republic
CityPrague
Period22/09/2124/09/21
Internet address

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