Comparison of different weighting schemes for the k NN classifier on time-series data

Many well-known machine learning algorithms have been applied to the task of time-series classification, including decision trees, neural networks, support vector machines and others. However, it was shown that the simple 1-nearest neighbor (1NN) classifier, coupled with an elastic distance measure like Dynamic Time Warping (DTW), often produces better results than more complex classifiers on time-series data, including k-nearest neighbor ( k NN) for values of k>1 . In this article, we revisit the k NN classifier on time-series data by considering ten classic distance-based vote weighting schemes in the context of Euclidean distance, as well as four commonly used elastic distance measures: DTW, Longest Common Subsequence, Edit Distance with Real Penalty and Edit Distance on Real sequence. Through experiments on the complete collection of UCR time-series datasets, we confirm the view that the 1NN classifier is very hard to beat. Overall, for all considered distance measures, we found that variants of the Dudani weighting scheme produced the best results.