Large scale similarity-based time series mining

• Autor(a) principal: Silva, Diego Furtado
• Data de Publicação: 2017
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-07122017-161346/
• topic: Data mining; Dynamic Time Warping; Dynamic Time Warping; Medidas de similaridade; Mineração de dados; Séries temporais; Similarity measures; Time series

Time series are ubiquitous in the day-by-day of human beings. A diversity of application domains generate data arranged in time, such as medicine, biology, economics, and signal processing. Due to the great interest in time series, a large variety of methods for mining temporal data has been proposed in recent decades. Several of these methods have one characteristic in common: in their cores, there is a (dis)similarity function used to compare the time series. Dynamic Time Warping (DTW) is arguably the most relevant, studied and applied distance measure for time series analysis. The main drawback of DTW is its computational complexity. At the same time, there are a significant number of data mining tasks, such as motif discovery, which requires a quadratic number of distance computations. These tasks are time intensive even for less expensive distance measures, like the Euclidean Distance. This thesis focus on developing fast algorithms that allow large-scale analysis of temporal data, using similarity-based methods for time series data mining. The contributions of this work have implications in several data mining tasks, such as classification, clustering and motif discovery. Specifically, the main contributions of this thesis are the following: (i) an algorithm to speed up the exact DTW calculation and its embedding into the similarity search procedure; (ii) a novel DTW-based spurious prefix and suffix invariant distance; (iii) a music similarity representation with implications on several music mining tasks, and a fast algorithm to compute it, and; (iv) an efficient and anytime method to find motifs and discords under the proposed prefix and suffix invariant DTW.