An Improvement of PIP for Time Series Dimensionality Reduction and Its Index Structure

Abstract

In this paper, we introduce a new time series dimensionality reduction method, IPIP. This method takes full advantages of PIP (Perceptually Important Points) method, proposed by Chung et al., with some improvements in order that the new method can theoretically satisfy the lower bounding condition for time series dimensionality reduction methods. Furthermore, we can make IPIP index able by showing that a time series compressed by IPIP can be indexed with the support of a multidimensional index structure based on Skyline index. Our experiments show that our IPIP method with its appropriate index structure can perform better than to some previous schemes, namely PAA based on traditional R*- tree.