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Detection of Periodic Components from Seasonal Time Series with Moving Trend Method and Low Pass Filtering

  • Jan T. Duda
  • Tomasz Pełech-PilichowskiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 635)

Abstract

The paper presents the concept of time series decomposition by splitting into components with linear filtering methods. The modified moving trend algorithm (MTF) allows for more precise specification of desired trend properties and periodic component extraction from seasonal time series. In the paper the time and frequency properties of classical and modified FIR filters are presented and confronted with 4th order Butterworth filter. Three examples of empirical, seasonal time series are treated with the analyzed filters. Advantages and drawbacks of the proposed filters concerning the cyclic component extraction efficiency are discussed on the base of the processing results shown in time and frequency domain. Recommendations for the appropriate moving-trend bases filter selection suitable for processed time series properties are presented.

Keywords

Time series processing Moving trend filtering 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.AGH University of Science and TechnologyKrakowPoland

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