Varying length time series classification

Most time series classification algorithms assume that the series are of uniform length. However, it is common for real world time series data to have unequal lengths. Differing time series lengths may arise from a number of fundamentally different mechanisms. In this work, we identify and evaluate two classes of such mechanisms -- variations in sampling rate relative to the relevant signal and variations between the start and end points of one time series relative to one another. We also investigate how time series generated by each of these classes of mechanism are best addressed in time series classification. We perform extensive experiments and provide practical recommendations on how variations in length should be handled in time series classification.