Detecting Short-Term Cycles in Complex Time Series Databases

Abstract

Time series characterize a large part of the data stored in financial, medical and scientific databases. The automatic statistical modelling of such data may be a very hard problem when the time series show “complex” features, such as nonlinearity, local nonstationarity, high frequency, long memory and periodic components. In such a context, the aim of this paper is to analyze the problem of detecting automatically the different periodic components in the data, with particular attention to the short term components (weakly, daily and intra-daily cycles). We focus on the analysis of real time series from a large database provided by an Italian electric company. This database shows complex features, either for the high dimension or the structure of the underlying process. A new classification procedure we proposed recently, based on a spectral analysis of the time series, was applied on the data. Here we perform a sensitivity analysis for the main tuning parameters of the procedure. A method for the selection of the optimal partition is then proposed.