Research projects

Project CEMAPRE internal

TitleFragmented-periodogram and fragmented-autocorrelation approaches for clustering big data time series
ParticipantsAndreia Albino, Jorge Caiado (Principal Investigator), Nuno Crato
SummaryWe will formally 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 recently introduced and discussed in the literature. In both procedures, 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 using the autocorrelation function of the time series computed
only around the lags of greatest interest.
Then, we will study this method in a few ways. First, we will argue theoretically that fragmenting
the autocorrelation function can have efficiency advantages when comparing time series. Second, by
means of a large simulation study, we will explore how the suggested procedure is able to condense
the relevant information of the time series. We will then compare its results with those from the
frequency domain counterpart. Third, we will 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.