GENETIC ALGORITHMS AND CROSS-CORRELATION CLUSTERING OF TIME SERIES
Roberto Baragona · 2007
It may turn of interest the problem of finding a proper partition of a set of time series into clusters, where all time series that belong to the same cluster are significantly correlated each other. An obvious difficulty arises because the cross-correlation between each pair of time series is a function of the time lag. Several dissimilarity indexes which take into account the cross-correlation function are considered and compared in order to point out their relative merits and effectiveness in recovering the true cluster structure. A suitable internal criterion for evaluating the computed partition is presented, and its maximisation by means of a genetic algorithm is proposed. A simulation experiment is performed that may suggest what dissimilarity measure is best and what parameters had better selected for implementing the genetic algorithm. Comparison with the well-known and widely used single linkage method, and with the artificial neural network procedure which introduced into pr...