Premium
Time series clustering and classification via frequency domain methods
Author(s) -
Holan Scott H.,
Ravishanker Nalini
Publication year - 2018
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1444
Subject(s) - cluster analysis , computer science , data mining , series (stratigraphy) , exploratory data analysis , bayesian probability , time series , machine learning , artificial intelligence , frequency domain , context (archaeology) , pattern recognition (psychology) , paleontology , computer vision , biology
Technological innovations combined with various scientific inquiries have resulted in a broad array of classification and clustering applications. Many of these applications directly involve classifying or clustering time series and have leveraged recent methodological advances within the frequency (spectral) domain. This paper reviews methods for clustering/classifying time series in the frequency domain and, in particular, describes various methods for different types of time series ranging from linear and stationary to nonlinear and nonstationary. Our perspective is cast from the statistics/data science literature and does not migrate into the literature on signal processing. Finally, we also summarize various aspects related to implementation, thereby providing the necessary context for interested practitioners. This article is categorized under: Statistical Models > Time Series Models Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory