Autocorrelation-based fuzzy clustering of time series

doi:10.1016/j.fss.2009.04.013

Fuzzy Sets and Systems

Volume 160, Issue 24, 16 December 2009, Pages 3565-3589

https://doi.org/10.1016/j.fss.2009.04.013 Get rights and content

Abstract

The traditional approaches to clustering a set of time series are generally applicable if there is a fixed underlying structure to the time series so that each will belong to one cluster or the other. However, time series often display dynamic behaviour in their evolution over time. This dynamic behaviour should be taken into account when attempting to cluster time series. For instance, during a certain period, a time series might belong to a certain cluster; afterwards its dynamics might be closer to that of another cluster. In this case, the traditional clustering approaches are unlikely to find and represent the underlying structure in the given time series. This switch from one time state to another, which is typically vague, can be naturally treated following a fuzzy approach. This paper proposes a fuzzy clustering approach based on the autocorrelation functions of time series, in which each time series is not assigned exclusively to only one cluster, but it is allowed to belong to different clusters with various membership degrees.

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Autocorrelation-based fuzzy clustering of time series

Abstract

Computational Statistics & Data Analysis

Computational Statistics & Data Analysis

Computational Statistics & Data Analysis

Mechanical Systems and Signal Processing

Computational Statistics & Data Analysis

Fuzzy Sets and Systems

Pattern Recognition

Fuzzy Sets and Systems

Chemometrics and Intelligent Laboratory Systems

Adaptive Control Processes: A Guided Tour

Pattern Recognition with Fuzzy Objective Function Algorithms

Convergence theory for fuzzy C-means: counterexamples and repairs

IEEE Transactions on Systems, Man and Cybernetics

Time Series Analysis. Forecasting and Control

Efficient implementation of the fuzzy C-means clustering algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence

The geometric approach to the comparison of multivariate time trajectories

Structural damage detection by fuzzy clustering

Mechanical Systems and Signal Processing