Robust Extrema Features for Time-Series Data Analysis
The extraction of robust features for comparing and analyzing time-series is a fundamentally important problem. Research efforts in this area encompass dimensionality reduction using popular signal analysis tools such as the discrete Fourier and wavelet transforms, various distance metrics, and the extraction of interest points from time-series. Recently, extrema features for analysis of time-series data have assumed increasing significance because of their natural robustness under a variety of practical distortions, their economy of representation and computational benefits. Invariably, the process of encoding extrema features is preceded by filtering of the time-series with an intuitively motivated filter (e.g. for smoothing), and subsequent thresholding to identify robust extrema.