Time series — A taxonomy based survey

The interest in time-series classification has increased in the last decade. Most of the proposals introduced new algorithms for classification, clustering and prediction. Most of the algorithms are dealing with two major aspects: dimensionality reduction techniques (ex: Piecewise Approximation Aggregation, Symbolic Aggregate Approximation etc.) and similarity measures (ex: Euclidean distance, Dynamic Time Warping etc.). In this article, we give an overview on the advantages and disadvantages of these algorithms.