Grasp heuristic for time series compression with piecewise aggregate approximation

Abstract

The Piecewise Aggregate Approximation (PAA) is widely used in time series data mining because it allows to discretize, to reduce the length of time series and it is used as a subroutine by algorithms for patterns discovery, indexing, and classification of time series. However, it requires setting one parameter: the number of segments to consider during the discretization. The optimal parameter value is highly data dependent in particular on large time series. This paper presents a heuristic for time series compression with PAA which minimizes the loss of information. The heuristic is built upon the well known metaheuristic GRASP and strengthened with an inclusion of specific global search component. An extensive experimental evaluation on several time series datasets demonstrated its efficiency and effectiveness in terms of compression ratio, compression interpretability and classification.