Multi-resolution Approach to Time Series Retrieval

We propose a new multi-resolution indexing and retrieval method of the similarity search problem in time series databases. The proposed method is based on a fast-and-dirty filtering scheme that iteratively reduces the search space using several resolution levels. For each resolution level the time series are approximated by an appropriate function. The distance between the time series and the approximating function is computed and stored at indexing-time. At query-time, assigned filters use these pre computed distances to exclude wide regions of the search space, which do not contain answers to the query, using the least number of query-time distance computations. The resolution level is progressively increased to converge towards higher resolution levels where the exclusion power rises, but the cost of query-time distance computations also increases. The proposed method uses lower bounding distances, so there are no false dismissals, and the search process returns all the possible answers to the query. A post-processing scanning on the candidate response set is performed to filter out any false alarms and return the final response set. We present experimentations that compare our method with sequential scanning on different datasets, using different threshold values and different approximating functions. The experiments show that our new method is faster than sequential scanning by an order of magnitude.