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Keywords:

  • signal analysis;
  • distance-based detectors;
  • time-series prediction;
  • immune-like event detection

Abstract

In the paper, a novel two-level algorithm of time-series change detection is presented. In the first level, to identify non-stationary sequences in a processed signal, preliminary detection of events is performed with a short-term prediction comparison. In the second stage, to confirm the changes detected in the first level, a similarity method aimed at identification of unique changes is employed. The detection of changes in a non-stationary time series is discussed, implemented algorithms are described and the results produced on a sample four financial time series are shown. General conditions for implementing the proposed algorithm as an immune-like event detector are discussed.