Change Direction Filtering Method of Nonstationary Time Series Using an Improved Sine Wave Indicator

2021 ◽  
Vol 141 (8) ◽  
pp. 927-939
Author(s):  
Hirokazu Yoshida
Keyword(s):  
Time Series ◽  
Sine Wave ◽  
Nonstationary Time Series ◽  
Filtering Method ◽  
Change Direction

scholarly journals G-Filtering Nonstationary Time Series

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Mengyuan Xu ◽  
Krista B. Cohlmia ◽  
Wayne A. Woodward ◽  
Henry L. Gray
Keyword(s):  
Time Series ◽  
Stationary Process ◽  
Nonstationary Time Series ◽  
Linear Filter ◽  
Time Varying ◽  
Linear Filtering ◽  
Original Process ◽  
Filtering Method ◽  
Time Deformation ◽  
Time Varying Frequency

The classical linear filter can successfully filter the components from a time series for which the frequency content does not change with time, and those nonstationary time series with time-varying frequency (TVF) components that do not overlap. However, for many types of nonstationary time series, the TVF components often overlap in time. In such a situation, the classical linear filtering method fails to extract components from the original process. In this paper, we introduce and theoretically develop the G-filter based on a time-deformation technique. Simulation examples and a real bat echolocation example illustrate that the G-filter can successfully filter a G-stationary process whose TVF components overlap with time.

scholarly journals Low-Pass Filtering Method for Poisson Data Time Series

2021 ◽  
Vol 11 (10) ◽  
pp. 4524
Author(s):  
Victor Getmanov ◽  
Vladislav Chinkin ◽  
Roman Sidorov ◽  
Alexei Gvishiani ◽  
Mikhail Dobrovolsky ◽  
...  
Keyword(s):  
Time Series ◽  
Finite Impulse Response ◽  
Digital Processing ◽  
Distributed Data ◽  
Gaussian Filter ◽  
Experimental Physics ◽  
Filtering Method ◽  
Filter Synthesis ◽  
Low Pass ◽  
Poisson Data

Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented.

Segmenting mean-nonstationary time series via trending regressions

2012 ◽  
Vol 168 (2) ◽  
pp. 367-381 ◽  
Author(s):  
Alexander Aue ◽  
Lajos Horváth ◽  
Marie Hušková
Keyword(s):  
Time Series ◽  
Nonstationary Time Series
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