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- W3060754773 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 $mathcal{O}(nlog n)$. 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." @default.
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- W3060754773 date "2019-01-01" @default.
- W3060754773 modified "2023-09-27" @default.
- W3060754773 title "A Formally Robust Time Series Distance Metric" @default.
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