Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms?

Large, James, Southam, Paul and Bagnall, Anthony (2019) Can Automated Smoothing Significantly Improve Benchmark Time Series Classification Algorithms? In: International Conference on Hybrid Artificial Intelligence Systems. Lecture Notes in Computer Science . Springer, pp. 50-60. ISBN 978-3-030-29858-6

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Abstract

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

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: LivePure Connector
Date Deposited: 13 Sep 2019 00:07
Last Modified: 22 Apr 2020 11:23
URI: https://ueaeprints.uea.ac.uk/id/eprint/72156
DOI: 10.1007/978-3-030-29859-3_5

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