Impact of Warping vs Smoothing for Time Series Similarity

Introduction. When dealing with time series, the application of a smoothing filter (to get rid of random fluctuations and better recognise the relevant structure) is usually one of the first steps. In the literature on time series similarity measures, however, the impact of smoothing is not explicitly or systematically considered – despite extensive experiments in, e.g., [2]. Instead, complex similarity measures are frequently applied (e.g. dynamic time warping (DTW)), which implicitly deal with noise, but mainly with temporal dilation and translation effects. So up to now it is unclear, to what extent the good performance of DTW is due to its smoothing or warping capabilities.