A Joint Soft Warping and Clustering Approach to Detecting Time Series Anomalies

Date
2021-12Author
Schuchmann, Chris John
Schuchmann, Christopher John
Metadata
Show full item recordAbstract
Unsupervised anomalous time series detection methods focus on identifying outliers
without prior knowledge of the dataset. However, these methods often require
multiple parameters to be optimized, with adequate performance tied to their careful
tuning and prior domain knowledge. In this work, two methods are proposed for
detecting outlier time series that adopt a joint clustering and alignment optimization
to filter out the desired signals. The time series are globally clustered while
simultaneously being aligned to other signals in their same cluster group. This
alternating optimization employs time-warping similarity measures to help identify
closely matching time series as well as the outliers. The proposed techniques
require minimal parameter tuning and yield superior results on many benchmark
datasets.