Novel Features for Time Series Analysis: A Complex Networks Approach

TL;DR

This paper introduces NetF, a novel feature set based on complex network measures for time series analysis, which is data-agnostic and improves clustering accuracy over traditional features.

Contribution

The work presents NetF, a new set of features derived from complex network mappings of time series, enhancing characterization and clustering without data preprocessing.

Findings

NetF captures diverse properties of time series effectively.

NetF outperforms conventional features in clustering benchmarks.

Network features provide a rich, complementary perspective for time series analysis.

Abstract

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.