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A Comparison of k-NN Methods for Time Series Classification and Regression

Interpretability is as important in data analytics on time series data as in other areas of analytics. For this reason, k-Nearest Neighbour methods have an important part to play; however coming up with effective similarity/distance measures is not straightforward given the nature of time series data. In this paper we survey the state-of-the-art distance measures used for time series analysis and evaluate their effectiveness on sample regression and classification tasks. In some circumstances, a ‘global’ method such as Dynamic Time Warping is effective, whereas for other datasets ‘feature-based’ methods work better.