Representing financial time series based on data point importance
Engineering Applications of Artificial Intelligence
A time-domain-constrained fuzzy clustering method and its application to signal analysis
Fuzzy Sets and Systems
Clustering of time series data-a survey
Pattern Recognition
A review on time series data mining
Engineering Applications of Artificial Intelligence
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The object of this paper is to present a model and a set of algorithms for estimating the parameters of a nonstationary time series generated by a continuous change in regime. We apply fuzzy clustering methods to the task of estimating the continuous drift in the time series distribution and interpret the resulting temporal membership matrix as weights in a time varying, mixture probability distribution function (PDF). We analyze the stopping conditions of the algorithm to infer a novel cluster validity criterion for fuzzy clustering algorithms of temporal patterns. The algorithm performance is demonstrated with three different types of signals