A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases

Abstract

We address the problem of similarity search in large time series databases. We introduce a novel-dimensionality reduction technique that supports an indexing algorithm that is more than an order of magnitude faster than the previous best known method. In addition to being much faster our approach has numerous other advantages. It is simple to understand and implement, allows more flexible distance measures including weighted Euclidean queries and the index can be built in linear time. We call our approach PCA-indexing (Piece-wise Constant Approximation) and experimentally validate it on space telemetry, financial, astronomical, medical and synthetic data.