# Time Series Classification via Topological Data Analysis

arXiv: https://arxiv.org/abs/2102.01956 · HTML: https://mlsanity.com/paper/2102.01956/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [A roadmap for the computation of persistent homology](https://mlsanity.com/paper/1506.08903/) (2015)
- [A persistent homology approach to heart rate variability analysis with an application to sleep-wake classification](https://mlsanity.com/paper/1908.06856/) (2019)
- [(Quasi)Periodicity Quantification in Video Data, Using Topology](https://mlsanity.com/paper/1704.08382/) (2017)
- [Topological Time Series Analysis](https://mlsanity.com/paper/1812.05143/) (2018)
- [Classification of Single-lead Electrocardiograms: TDA Informed Machine Learning](https://mlsanity.com/paper/1911.12253/) (2019)

## Built on by (most cited first, 2 of 2)

- [On the Generalizability of ECG-based Stress Detection Models](https://mlsanity.com/paper/2210.06225/) (2022)
- [Sparse Portfolio Selection via Topological Data Analysis based Clustering](https://mlsanity.com/paper/2401.16920/) (2024)

## Abstract (arXiv)

In this paper, we develop topological data analysis methods for classification tasks on univariate time series. As an application, we perform binary and ternary classification tasks on two public datasets that consist of physiological signals collected under stress and non-stress conditions. We accomplish our goal by using persistent homology to engineer stable topological features after we use a time delay embedding of the signals and perform a subwindowing instead of using windows of fixed length. The combination of methods we use can be applied to any univariate time series and in this application allows us to reduce noise and use long window sizes without incurring an extra computational cost. We then use machine learning models on the features we algorithmically engineered to obtain higher accuracies with fewer features.