Paper Info
Title
Pattern Discovery In Time Series Using Autoencoder In Comparison To Nonlearning Approaches
Abstract
In technical systems the analysis of similar situations is a promising technique to gain information about the system's state, its health or wearing. Very often, situations cannot be defined but need to be discovered as recurrent patterns within time series data of the system under consideration. This paper addresses the assessment of different approaches to discover frequent variable-length patterns in time series. Because of the success of artificial neural networks (NN) in various research fields, a special issue of this work is the applicability of NNs to the problem of pattern discovery in time series. Therefore we applied and adapted a Convolutional Autoencoder and compared it to classical nonlearning approaches based on Dynamic Time Warping, based on time series discretization as well as based on the Matrix Profile. These nonlearning approaches have also been adapted, to fulfill our requirements like the discovery of potentially time scaled patterns from noisy time series. We showed the performance (quality, computing time, effort of parametrization) of those approaches in an extensive test with synthetic data sets. Additionally the transferability to other data sets is tested by using real life vehicle data. We demonstrated the ability of Convolutional Autoencoders to discover patterns in an unsupervised way. Furthermore the tests showed, that the Autoencoder is able to discover patterns with a similar quality like classical nonlearning approaches.
Year
DOI
Venue
2021
10.3233/ICA-210650
INTEGRATED COMPUTER-AIDED ENGINEERING
Keywords
DocType
Volume
Time series data mining, pattern discovery, motif discovery, autoencoder, unsupervised
Journal
28
Issue
ISSN
Citations 
3
1069-2509
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Fabian Kai-Dietrich Noering100.68
Yannik Schroeder200.34
Konstantin Jonas300.34
Frank Klawonn400.34