Variable length queries for time series data

Finding similar patterns in a time sequence is a well-studied problem. Most of the current techniques work well for queries of a prespecified length, but not for variable length queries. We propose a new indexing technique that works well for variable length queries. The central idea is to store index structures at different resolutions for a given dataset. The resolutions are based on wavelets. For a given query, a number of subqueries at different resolutions are generated. The ranges of the subqueries are progressively refined based on results from previous subqueries. Our experiments show that the total cost for our method is 4 to 20 times less than the current techniques including linear scan. Because of the need to store information at multiple resolution levels, the storage requirement of our method could potentially be large. In the second part of the paper we show how the index information can be compressed with minimal information loss. According to our experimental results, even after compressing the size of the index to one fifth, the total cost of our method is 3 to 15 times less than the current techniques.