# Novel Features for Time Series Analysis: A Complex Networks Approach

arXiv: https://arxiv.org/abs/2110.09888 · HTML: https://mlsanity.com/paper/2110.09888/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 67 references that are in the map.
- First posted on arXiv 11 Oct 2021.

## Builds on (most cited first, 9 of 9)

- [catch22: CAnonical Time-series CHaracteristics](https://mlsanity.com/paper/1901.10200/) (2019)
- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [Highly comparative feature-based time-series classification](https://mlsanity.com/paper/1401.3531/) (2014)
- [Clustering and Community Detection in Directed Networks: A Survey](https://mlsanity.com/paper/1308.0971/) (2013)
- [Highly comparative time-series analysis: The empirical structure of time series and their methods](https://mlsanity.com/paper/1304.1209/) (2013)
- [Feature-based time-series analysis](https://mlsanity.com/paper/1709.08055/) (2017)
- [Automatic time-series phenotyping using massive feature extraction](https://mlsanity.com/paper/1612.05296/) (2016)
- [Multiplex visibility graphs to investigate recurrent neural networks dynamics](https://mlsanity.com/paper/1609.03068/) (2016)
- [Consistency and differences between centrality measures across distinct classes of networks](https://mlsanity.com/paper/1805.02375/) (2018)

## Built on by (most cited first, 6 of 6)

- [Persistent Homology of Coarse Grained State Space Networks](https://mlsanity.com/paper/2206.02530/) (2022)
- [Identifying Stochasticity in Time-Series with Autoencoder-Based Content-aware 2D Representation: Application to Black Hole Data](https://mlsanity.com/paper/2304.11560/) (2023)
- [COSTI: a New Classifier for Sequences of Temporal Intervals](https://mlsanity.com/paper/2204.13467/) (2022)
- [Identification of Stochasticity by Matrix-decomposition: Applied on Black Hole Data](https://mlsanity.com/paper/2307.07703/) (2023)
- [Experiential-Informed Data Reconstruction for Fishery Sustainability and Policies in the Azores](https://mlsanity.com/paper/2309.09326/) (2023)
- [Synthetic Time Series Generation via Complex Networks](https://mlsanity.com/paper/2601.22879/) (2026)

## Abstract (arXiv)

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.