Similarity search based on random projection for high frequency time series

Similarity search in high frequency time series of domains as diverse as finance, marketing and industry has attracted much research attention recently. The main notions used in similarity search for time series are defined in a formal way. And a fast algorithm of similarity search based on random projection for high frequency time series is proposed. In order to achieve the high-level representation of time series, this algorithm uses the random projection method to map the original time series to the lower space. Then, the spatial data index structure such as R* tree is built using the high-level representation of the original time series, and the Euclidean distance is used as the similarity measurement. It is a fast similarity searching algorithm with high accuracy for high frequency time series. The experimental results demonstrate that the method is effective and efficient.