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.