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arXiv · 2008.07865

A Formally Robust Time Series Distance Metric

Maximilian Toller·Bernhard C. Geiger·Roman Kern
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Abstract

Distance-based classification is among the most competitive classification methods for time series data. The most critical component of distance-based classification is the selected distance function. Past research has proposed various different distance metrics or measures dedicated to particular aspects of real-world time series data, yet there is an important aspect that has not been considered so far: Robustness against arbitrary data contamination. In this work, we propose a novel distance metric that is robust against arbitrarily "bad" contamination and has a worst-case computational complexity of $\mathcal{O}(n\log n)$. We formally argue why our proposed metric is robust, and demonstrate in an empirical evaluation that the metric yields competitive classification accuracy when applied in k-Nearest Neighbor time series classification.

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Maximilian Toller, Bernhard C. Geiger, Roman Kern. 2020-08-18. A Formally Robust Time Series Distance Metric. https://arxiv.org/abs/2008.07865

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