A fragmented-periodogram approach for clustering big data time series

Jorge Caiado, Nuno Crato () and Pilar Poncela

Advances in Data Analysis and Classification, 2020, vol. 14, issue 1, No 7, 117-146

Abstract: Abstract We propose and study a new frequency-domain procedure for characterizing and comparing large sets of long time series. Instead of using all the information available from data, which would be computationally very expensive, we propose some regularization rules in order to select and summarize the most relevant information for clustering purposes. Essentially, we suggest to use a fragmented periodogram computed around the driving cyclical components of interest and to compare the various estimates. This procedure is computationally simple, but able to condense relevant information of the time series. A simulation exercise shows that the smoothed fragmented periodogram works in general better than the non-smoothed one and not worse than the complete periodogram for medium to large sample sizes. We illustrate this procedure in a study of the evolution of several stock markets indices. We further show the effect of recent financial crises over these indices behaviour.

Keywords: Big data; Fragmented periodogram; Spectral clustering; Smoothed periodogram; Time series clustering; 62H30; 62M10; 62M15 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11634-019-00365-8

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