Effectiveness of similarity measures in classification of time series data with intrinsic and extrinsic variability

Time series are hard to analyse because of their intrinsic variability which arises from the stochastic nature of the underlying process. Analysis is harder still if the underlying process is non-stationary. Further extrinsic variation may be imposed by the variability of the sampling process, e.g. by sampling at different or non-uniform time intervals. We explore the efficacy of some common distance/similarity measures - Euclidean (EUC), Neighbourhood Counting Metric (NCM), Dynamic Time Warping (DTW), Longest Common Subsequence (LCS) and All Common Subsequences (ACS) - in a nearest neighbour classifier for classifying time series data with and without extrinsic variability. An artificial dataset containing trajectories of a 2-dimensional dynamical system and a real dataset, the Australian Sign Language Dataset (AUSLAN), are explored.