Grid-based indexing for large time series databases

Abstract

Similarity search in large time series databases is an interesting and challenging problem. Because of the high dimensional nature of the data, the difficulties associated with dimensionality curse arise. The most promising solution is to use dimensionality reduction, and construct a multi-dimensional index structure for the reduced data. In this work we introduce a new approach called grid-based Datawise Dimensionality Reduction(DDR) which attempts to preserve the characteristics of time series. We then apply quantization to construct an index structure. An experimental comparison with existing techniques demonstrate the utility of our approach. © Springer-Verlag 2003.