Time series clustering using fragmented autocorrelations

Andreia Albino, Jorge Caiado and Nuno Crato ()

Physica A: Statistical Mechanics and its Applications, 2024, vol. 650, issue C

Abstract: We propose and study an autocorrelation procedure designed to characterize and compare large sets of long time series. This time-domain procedure is contrasted with a frequency-domain approach that has recently been introduced and discussed in the literature. In both cases, instead of using all the information available from data, which would be computationally very expensive, adequate regularization rules select and summarize the most relevant information suitable for clustering purposes. Essentially, we suggest to use the autocorrelation coefficients of the time series that are only computed around the lags of greatest interest. Then, we study this method in several ways. We argue theoretically that fragmenting the autocorrelation function can have efficiency advantages when comparing time series. By means of a large simulation study, we show that the suggested procedure can condense the relevant information of the time series. We compare its results with those from the frequency domain counterpart. We further illustrate this procedure in a study of the evolution of several stock markets indices and show the effect of recent financial crises on the behaviour of these indices.

Keywords: Big data; Fragmented periodogram; Spectral clustering; Fragmented autocorrelation; Time series clustering (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:650:y:2024:i:c:s0378437124004904

DOI: 10.1016/j.physa.2024.129981

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