Time series data mining using complex networks

• Ano de defesa: 2017
• Autor(a) principal: Ferreira, Leonardo Nascimento
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Agrupamento; Classificação; Classification; Clustering; Community detection; Complex networks; Detecção de comunidades; Detecção de periodicidade; Graph mining; Mineração de grafos; Mineração de séries temporais; Periodicity detection; Redes complexas; Time series data mining
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-01022018-144118/

A time series is a time-ordered dataset. Due to its ubiquity, time series analysis is interesting for many scientific fields. Time series data mining is a research area that is intended to extract information from these time-related data. To achieve it, different models are used to describe series and search for patterns. One approach for modeling temporal data is by using complex networks. In this case, temporal data are mapped to a topological space that allows data exploration using network techniques. In this thesis, we present solutions for time series data mining tasks using complex networks. The primary goal was to evaluate the benefits of using network theory to extract information from temporal data. We focused on three mining tasks. (1) In the clustering task, we represented every time series by a vertex and we connected vertices that represent similar time series. We used community detection algorithms to cluster similar series. Results show that this approach presents better results than traditional clustering results. (2) In the classification task, we mapped every labeled time series in a database to a visibility graph. We performed classification by transforming an unlabeled time series to a visibility graph and comparing it to the labeled graphs using a distance function. The new label is the most frequent label in the k-nearest graphs. (3) In the periodicity detection task, we first transform a time series into a visibility graph. Local maxima in a time series are usually mapped to highly connected vertices that link two communities. We used the community structure to propose a periodicity detection algorithm in time series. This method is robust to noisy data and does not require parameters. With the methods and results presented in this thesis, we conclude that network science is beneficial to time series data mining. Moreover, this approach can provide better results than traditional methods. It is a new form of extracting information from time series and can be easily extended to other tasks.