# An Empirical Evaluation of Similarity Measures for Time Series Classification

arXiv: https://arxiv.org/abs/1401.3973 · HTML: https://mlsanity.com/paper/1401.3973/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2014.

## Built on by (most cited first, 12 of 12)

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- [Semantic of Cloud Computing services for Time Series workflows](https://mlsanity.com/paper/2202.00609/) (2022)
- [Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm](https://mlsanity.com/paper/2209.04213/) (2022)
- [Continuous Edit Distance, Geodesics and Barycenters of Time-varying Persistence Diagrams](https://mlsanity.com/paper/2512.12939/) (2025)
- [A Regime-Aware Fusion Framework for Time Series Classification](https://mlsanity.com/paper/2512.15378/) (2025)

## Abstract (arXiv)

Time series are ubiquitous, and a measure to assess their similarity is a core part of many computational systems. In particular, the similarity measure is the most essential ingredient of time series clustering and classification systems. Because of this importance, countless approaches to estimate time series similarity have been proposed. However, there is a lack of comparative studies using empirical, rigorous, quantitative, and large-scale assessment strategies. In this article, we provide an extensive evaluation of similarity measures for time series classification following the aforementioned principles. We consider 7 different measures coming from alternative measure `families', and 45 publicly-available time series data sets coming from a wide variety of scientific domains. We focus on out-of-sample classification accuracy, but in-sample accuracies and parameter choices are also discussed. Our work is based on rigorous evaluation methodologies and includes the use of powerful statistical significance tests to derive meaningful conclusions. The obtained results show the equivalence, in terms of accuracy, of a number of measures, but with one single candidate outperforming the rest. Such findings, together with the followed methodology, invite researchers on the field to adopt a more consistent evaluation criteria and a more informed decision regarding the baseline measures to which new developments should be compared.