An Improvement of PAA for Dimensionality Reduction in Large Time Series Databases

Abstract

Many dimensionality reduction techniques have been proposed for effective representation of time series data. Piecewise Aggregate Approximation (PAA) is one of the most popular methods for time series dimensionality reduction. While PAA approach allows a very good dimensionality reduction, PAA minimizes dimensionality by the mean values of equal sized frames. This mean value based representation may cause a high possibility to miss some important patterns in some time series datasets. In this work, we propose a new approach based on PAA, which we call Piecewise Linear Aggregate Approximation (PLAA). PLAA is the combination of a mean-based and a slope-based dimensionality reduction. We show that PLAA can improve representation preciseness through a better tightness of lower bound in comparison to PAA.