Aggressive pruning strategy for time series retrieval using a multi-resolution representation based on vector quantization coupled with discrete wavelet transform

Time series representation methods are widely used to handle time series data by projecting them onto low-dimensional spaces where queries are processed. Multi-resolution representation methods speed up the similarity search process by using pre-computed distances, which are calculated and stored at the indexing stage and then used at the query stage, together with filters in the form of exclusion conditions. In this paper, we present a new multi-resolution representation method that combines the Haar wavelet-based multi-resolution method with vector quantization to maximize the pruning power of the similarity search algorithm. The new method is validated through extensive experiments on different datasets from several time series repositories. The results obtained prove the efficiency of the new method.