Smoothing and extracting trend and cyclic components of time series
Игорь Александрович Ботыгин, Valery A. Tartakovsky, Vladislav S. Sherstnev, Anna I. Sherstneva · 2022
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.