Research Area: Machine Learning
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.
Keywords:
Time Series Analysis
Deep Learning
Machine Learning
Author(s) Name: Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro & Fernando Silva
Journal name: Data Mining and Knowledge Discovery
Conferrence name:
Publisher name: Springer
DOI: 10.1007/s10618-022-00826-3
Volume Information: volume 36, pages1062–1101 (2022)
