Aggressive pruning strategy for time series retrieval using a multi‐resolution representation based on vector quantization coupled with discrete wavelet transform. Issue 1 (26th December 2016)

• Record Type: Journal Article
• Authors: Muhammad Fuad, Muhammad Marwan
• Other Names: Cortez Paulo guestEditor.; Santos Manuel Filipe guestEditor.; Hall Jon guestEditor.
• Is Part Of: Expert systems. Volume 34:Issue 1(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-12-26
• Subjects: dimensionality reduction -- Haar wavelets -- multi‐resolution -- time series -- vector quantization; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12171
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: 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.