A Formally Robust Time Series Distance Metric
This work addresses robustness issues in time series classification for applications with contaminated data, representing an incremental improvement over existing methods.
The authors tackled the problem of arbitrary data contamination in time series classification by proposing a novel robust distance metric, which achieves competitive classification accuracy in k-Nearest Neighbor evaluations.
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.