A Formally Robust Time Series Distance Metric
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.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationGeneral ClassificationTime SeriesTime Series AnalysisTime Series ClassificationSimilar Papers 제목 키워드 기반
On Clustering Time Series Using Euclidean Distance and Pearson Correlation
For time series comparisons, it has often been observed that z-score normalized Euclidean distances far outperform the unnormalized variant. In this paper we show that a z-score normalized, squared Euclidean Distance is,…
ClusteringGeneral ClassificationTime SeriesTime Series Analysis+1Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm
The classification of time series data is a well-studied problem with numerous practical applications, such as medical diagnosis and speech recognition. A popular and effective approach is to classify new time series in …
ClassificationDynamic Time WarpingGeneral ClassificationMedical Diagnosis+4The Wasserstein-Fourier Distance for Stationary Time Series
We propose the Wasserstein-Fourier (WF) distance to measure the (dis)similarity between time series by quantifying the displacement of their energy across frequencies. The WF distance operates by calculating the Wasserst…
Data AugmentationDimensionality ReductionTime SeriesTime Series Analysis+1DTWSSE: Data Augmentation with a Siamese Encoder for Time Series
Access to labeled time series data is often limited in the real world, which constrains the performance of deep learning models in the field of time series analysis. Data augmentation is an effective way to solve the pro…
Data AugmentationDecoderTime SeriesTime Series AnalysisAmercing: An Intuitive, Elegant and Effective Constraint for Dynamic Time Warping
Dynamic Time Warping (DTW), and its constrained (CDTW) and weighted (WDTW) variants, are time series distances with a wide range of applications. They minimize the cost of non-linear alignments between series. CDTW and W…
Dynamic Time WarpingTime SeriesTime Series AnalysisTime Series Classification