Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization

Raphael Fettaya, Dor Bank, Rachel Lemberg, Linoy Barel

Abstract: Change-point detection problem consists of discovering abrupt property changes in the generation process of time-series. Most state-of-the-art models are optimizing the power of a kernel two-sample test, with only a few assumptions on the distribution of the data. Unfortunately, because they presume the samples are distributed i.i.d, they are not able to use information about the seasonality of a time-series. In this paper, we present a novel approach - ATR-CSPD allowing the detection of changes in the seasonal pattern of a time-series. Our method uses an autoencoder together with a temporal regularization, to learn the pattern of each seasonal cycle. Using low dimensional representation of the seasonal patterns, it is possible to accurately and efficiently estimate the existence of a change point using a clustering algorithm. Through experiments on artificial and real-world data sets, we demonstrate the usefulness of the proposed method for several applications.

Code: https://anonymous.4open.science/r/3655aebd-63f0-4dd1-a5f8-be9dbb5ed060/

Keywords: Autoencoder, Change Point Detection, Timeseries