Comparison of low rank tensorial approximation to discrete wavelet approximation of given nonstationary time series
Approximation and filtration of time series belongs to one of most important problems in real word scientific application. Despite of plenty of
existed methods, they are effective in very specific situations, most of them assume that time series is stationary or can be stationary by finite
amount of differencing. In this article we will consider several time series and compare Singular spectrum analysis and its low rank tensorial
approximation method to classical wavelet decomposition approach.