Smoothing and extracting trend and cyclic components of time series

I. A. Botygin; V. A. Tartakovsky; V. S. Sherstnev; A. I. Sherstneva

doi:10.1117/12.2645396

7 December 2022 Smoothing and extracting trend and cyclic components of time series

I. A. Botygin, V. A. Tartakovsky, V. S. Sherstnev, A. I. Sherstneva

Proceedings Volume 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 123417O (2022) https://doi.org/10.1117/12.2645396
Event: 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 2022, Tomsk, Russia

Abstract

The results of a study noise filtering in temperature time series measuring channels using basic frequency filtering algorithms are presented. The study was carried out from the perspective of high frequency random noise suppression. Temperature time series filtering was compared using sliding window procedures with adaptive bandwidth selection, median sliding window, and Baxter-King band-pass approximation. Experimental comparative analysis of filtering efficiency was carried out using the statistical programming language R and open libraries with sliding regression methods. The recorders of surface atmosphere parameters were ultrasonic meteorological stations located at the test site of IMKES SB RAS.

Citation Download Citation

I. A. Botygin, V. A. Tartakovsky, V. S. Sherstnev, and A. I. Sherstneva "Smoothing and extracting trend and cyclic components of time series", Proc. SPIE 12341, 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 123417O (7 December 2022); https://doi.org/10.1117/12.2645396

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KEYWORDS

Digital filtering

Bandpass filters

Optical filters

Binary data

Linear filtering

Nonlinear filtering

Signal processing

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