Combining SAX and Piecewise Linear Approximation to Improve Similarity Search on Financial Time Series

Efficient and accurate similarity searching on a large time series data set is an important but non- trivial problem. In this work, we propose a new approach to improve the quality of similarity search on time series data by combining symbolic aggregate approximation (SAX) and piecewise linear approximation. The approach consists of three steps: transforming real valued time series sequences to symbolic strings via SAX, pattern matching on the symbolic strings and a post-processing via Piecewise Linear Approximation.