Comparative analysis of modern time-series analysis methods

Abstract

The purpose of this article is the analysis of a priori uncertainty elimination methods in a poorly studied processing experimental results interpretation under conditions of limited a priori knowledge about research process models. Two modern adaptive methods that can be used at experimental data preprocessing stage: empirical mode decomposition and singular spectral analysis (caterpillar) are presented. Comparative analysis of these two methods by power consumption analysis example is performed.