Time series classification with ensembles of elastic distance measures

Abstract

Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems. These include variants of dynamic time warping (DTW), such as weighted and derivative DTW, and edit distance-based measures, including longest common subsequence, edit distance with real penalty, time warp with edit, and move–split–merge. These measures have the common characteristic that they operate in the time domain and compensate for potential localised misalignment through some elastic adjustment. Our aim is to experimentally test two hypotheses related to these distance measures. Firstly, we test whether there is any significant difference in accuracy for TSC problems between nearest neighbour classifiers using these distance measures. Secondly, we test whether combining these elastic distance measures through simple ensemble schemes gives significantly better accuracy. We test these hypotheses by carrying out one of the largest experimental studies ever conducted into time series classification. Our first key finding is that there is no significant difference between the elastic distance measures in terms of classification accuracy on our data sets. Our second finding, and the major contribution of this work, is to define an ensemble classifier that significantly outperforms the individual classifiers. We also demonstrate that the ensemble is more accurate than approaches not based in the time domain. Nearly all TSC papers in the data mining literature cite DTW (with warping window set through cross validation) as the benchmark for comparison. We believe that our ensemble is the first ever classifier to significantly outperform DTW and as such raises the bar for future work in this area

Time series representation and similarity based on local autopatterns

“…Furthermore, LPS allows for a simple parallel implementation which makes it computationally efficient. Our approach provides fast and competitive results on benchmark datasets from the UCR time series database (Keogh et al 2011) and other published work (Frank and Asuncion 2010;Hills et al 2014;Lines and Bagnall 2014;Olszewski 2012;Rakthanmanon and Keogh 2013;Sübakan et al 2014;CMU 2012).…”

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

“…When computation time is not a problem, the best approach is to use a combination of nearest neighbour (NN) classifiers that use whole series elastic distance measures in the time domain and with first order derivatives: Elastic ensemble (EE) [15]. However, if a single measure is required a choice between DTW and MSM is recommended, with MSM preferred because of its overall performance.…”

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“…An ensemble of classifiers is a set of base classifiers whose individual decisions are combined to classify new examples [15]. Each classifier is allowed to independently observe the example and provide a tentative classification output, i.e., a vote.…”

“…Lines and Bagnall [15] employed different distance measures combined into an ensemble of 1-NN classifiers. Previous work by Oates et al [16] used the SAX representation of time series to compose ensembles where each base classifier was constructed with different parameters.…”