Novel features for time series analysis: a complex networks approach

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Silva, VF (Autor) Outra; Maria Eduarda Silva (Autor) FEP; Pedro Ribeiro (Autor) FCUP; Silva, F (Autor) FCUP

Revista

Título: Data Mining and Knowledge Discovery

Vol. 36

Páginas: 1062-1101

ISSN: 1384-5810

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 12 Citações

Scopus - 19 Citações

Outras Informações

ID Authenticus: P-00V-M16

DOI: 10.1007/s10618-022-00826-3

Abstract (EN): Being able to capture the characteristics of a time series with a feature vector is a very important task with a multitude of applications, such as classification, clustering or forecasting. Usually, the features are obtained from linear and nonlinear time series measures, that may present several data related drawbacks. In this work we introduce NetF as an alternative set of features, incorporating several representative topological measures of different complex networks mappings of the time series. Our approach does not require data preprocessing and is applicable regardless of any data characteristics. Exploring our novel feature vector, we are able to connect mapped network features to properties inherent in diversified time series models, showing that NetF can be useful to characterize time data. Furthermore, we also demonstrate the applicability of our methodology in clustering synthetic and benchmark time series sets, comparing its performance with more conventional features, showcasing how NetF can achieve high-accuracy clusters. Our results are very promising, with network features from different mapping methods capturing different properties of the time series, adding a different and rich feature set to the literature.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 40