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tl;dr: no, it cannot, at least not on average on the standard archiveproblems. We assess whether using six smoothing algorithms (moving average,exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourierapproximation and a recursive median sieve) could be automatically applied totime series classification problems as a preprocessing step to improve theperformance of three benchmark classifiers (1-Nearest Neighbour with Euclideanand Dynamic Time Warping distances, and Rotation Forest). We found nosignificant improvement over unsmoothed data even when we set the smoothingparameter through cross validation. We are not claiming smoothing has no worth.It has an important role in exploratory analysis and helps with specificclassification problems where domain knowledge can be exploited. What weobserve is that the automatic application does not help and that we cannotexplain the improvement of other time series classification algorithms over thebaseline classifiers simply as a function of the absence of smoothing.

Can automated smoothing significantly improve benchmark time series classification algorithms?