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Low-Frequency Signal Reconstruction and Abrupt Change Detection in Non-stationary Time Series by Enhanced Moving Trend Based Filters

  • Tomasz Pełech-PilichowskiEmail author
  • Jan T. Duda
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 579)

Abstract

An original approach to digital moving trend based filters (MTF) design, based on Bode plots analysis is proposed, aimed at seasonal time series decomposition and prediction [5]. A number of polynomials of different range are discussed to be used in the MTF as the LS approximation formula. The Bode plots of the MTF are shown, and the best filter is selected. Results of a seasonal time series decomposition and prediction with the best MTF is presented and compared to the classical MTF calculations (involving the linear LS approximation). The MTF enhancementsareintroduced aimed at better change detection. Efficiency of low-frequency periodic signals reconstruction and step-wise changes is illustrated.

Keywords

Periodic Component Cyclic Component Prediction Horizon Final Segment Nonstationary Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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