Time-series Change Point Detection with Self-Supervised Contrastive Predictive Coding

11/28/2020
by   Shohreh Deldari, et al.
3

Change Point Detection techniques aim to capture changes in trends and sequences in time-series data to describe the underlying behaviour of the system. Detecting changes and anomalies in the web services, the trend of applications usage can provide valuable insight towards the system, however, many existing approaches are done in a supervised manner, requiring well-labelled data. As the amount of data produced and captured by sensors are growing rapidly, it is getting harder and even impossible to annotate the data. Therefore, coming up with a self-supervised solution is a necessity these days. In this work, we propose TSCP2 a novel self-supervised technique for temporal change point detection, based on representation learning with Temporal Convolutional Network (TCN). To the best of our knowledge, our proposed method is the first method which employs Contrastive Learning for prediction with the aim change point detection. Through extensive evaluations, we demonstrate that our method outperforms multiple state-of-the-art change point detection and anomaly detection baselines, including those adopting either unsupervised or semi-supervised approach. TSCP2 is shown to improve both non-Deep learning- and Deep learning-based methods by 0.28 and 0.12 in terms of average F1-score across three datasets.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 10

07/22/2020

Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons

Change-point detection in a time series aims to discover the time points...
03/13/2020

An Evaluation of Change Point Detection Algorithms

Change point detection is an important part of time series analysis, as ...
11/10/2020

Statistical learning for change point and anomaly detection in graphs

Complex systems which can be represented in the form of static and dynam...
12/10/2021

Segmenting Time Series via Self-Normalization

We propose a novel and unified framework for change-point estimation in ...
12/08/2021

Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification

Machine-part interaction classification is a key capability required by ...
06/04/2021

Principled change point detection via representation learning

Change points are abrupt alterations in the distribution of sequential d...
02/18/2019

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

It is important to identify the change point of a system's health status...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.