Effect of window length with singular spectrum analysis in extracting the trend signal on rainfall data

Abstract

Time series data can be reconstructed in terms of signal and noise components through Singular Spectrum Analysis method (SSA). In SSA, window length selection is important in ensuring that the signal and noise components are clearly separated. In general, the window length should be large enough but not greater than half of the observed time series data. However, different observed behaviour of a dataset might influence the selection of window length. In this study, we demonstrate the effect of window length on torrential rainfall time series data at three different scales in extracting the trend signals in rainfall data. The window lengths are compared using the classical SSA and another variation of SSA called iterative O-SSA. We use the minimum value of w-correlation to identify the window length that best measure clear separability between components. We found that a window length to the number of observed data of 6 shows a trend that fits well to the original rainfall time series data.