Stochastic Time Series Representation for Interval Pattern Mining via Gaussian Processes
Fabian Berns, Christian Beecks · Society for Industrial and Applied Mathematics eBooks · 2021
Trends, periodicities and local variations are among the main recognizable patterns in time series data. While humans are able to quickly explore the superimposition of such patterns, mining algorithms are often faced with the challenge of finding (i) a suitable time series representation model and (ii) an expressive query model which adapt to diverse application domains and information needs. In this paper, we propose a supervised stochastic approach which facilitates interval-based pattern analysis of time series data. Our proposal is based on non-parametric Gaussian Processes and is able to interrelate interesting patterns within single and across multiple time series. Our performance evaluation in different real-world application domains indicates that our approach is able to expose interesting patterns and knowledge.