Experimental comparison of representation methods and distance measures for time series data

By Xiaoyue Wang, Abdullah Mueen, Hui Ding, Goce Trajcevski, Peter Scheuermann, Eamonn J. Keogh. Data mining and knowledge discovery, 2010

893 citations recorded by the source corpus.

The previous decade has brought a remarkable increase of the interest in applications that deal with querying and mining of time series data. Many of the research efforts in this context have focused on introducing new representation methods for dimensionality reduction or novel similarity measures for the underlying data. In the vast majority of cases, each individual work introducing a particular method has made specific claims and, aside from the occasional theoretical justifications, provided quantitative experimental observations. However, for the most part, the comparative aspects of these experiments were too narrowly focused on demonstrating the benefits of the proposed methods over some of the previously introduced ones. In order to provide a comprehensive validation, we conducted an extensive experimental study re-implementing eight different time series representations and nine similarity measures and their variants, and testing their effectiveness on 38 time series data sets from a wide variety of application domains. In this article, we give an overview of these different techniques and present our comparative experimental findings regarding their effectiveness. In addition to providing a unified validation of some of the existing achievements, our experiments also indicate that, in some cases, certain claims in the literature may be unduly optimistic.

Subfields: Signal Processing

View Paper: https://www.semanticscholar.org/paper/083658dde8d4d7831ec3e39a4739d3a163713844

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In Signal Processing

Citations in the corpus

k-Shape: Efficient and Accurate Clustering of Time Series (2015, methodology): “In particular, recent comprehensive and independent experimental evaluations of state-of-the-art distance measures for time-series comparison — 9 measures and their variants in [19, 81] and 48 measures in [26] — did not consider cross-correlation.”
Using dynamic time warping distances as features for improved time series classification (2016, methodology): “…that have been proposed for time series classification, the decades old dynamic time warping (DTW) in combination with one-nearest neighbor has not been easy to beat convincingly (Xi et al. 2006; Ding et al. 2008; Xing et al. 2010; Chen et al. 2013; Wang et al. 2013; Lines and Bagnall 2014).”
Fast Shapelets: A Scalable Algorithm for Discovering Time Series Shapelets (2013, methodology): “64%, which appears to be the best known result on this dataset [11][27].”
Time-series clustering - A decade review (2015, methodology): “Data adaptive representation methods are performed on all time-series in datasets and try to minimize the global reconstruction error [94] using arbitrary length (non-equal) segments.”
k-Shape: Efficient and Accurate Clustering of Time Series (2016, background): “In particular, recent comprehensive and independent experimental evaluations of state-of-the-art distance measures for time-series comparison — 9 measures and their variants in [15, 66] and 48 measures in [22] — did not consider cross-correlation.”
Highly comparative time-series analysis: the empirical structure of time series and their methods (2013, background): “Conventional measures of time-series similarity are often based on distances measured between the time-series values themselves and are hence restricted to sets of time series of a fixed length [5].”
The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances (2016, methodology): “However, in 2008, Ding et al. (2008) and Wang et al. (2013) evaluated eight different distance measures on 38 data sets and found none significantly better than DTW.”