# Topological Machine Learning for Multivariate Time Series

arXiv: https://arxiv.org/abs/1911.12082 · HTML: https://mlsanity.com/paper/1911.12082/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Topological Data Analysis (TDA) Techniques Enhance Hand Pose Classification from ECoG Neural Recordings](https://mlsanity.com/paper/2110.04653/) (2021)
- [Sparse Portfolio Selection via Topological Data Analysis based Clustering](https://mlsanity.com/paper/2401.16920/) (2024)
- [Global and Local Topology-Aware Attention with Persistent Homology and Euler Biases for Time-Series Forecasting](https://mlsanity.com/paper/2605.03163/) (2026)

## Abstract (arXiv)

We develop a framework for analyzing multivariate time series using topological data analysis (TDA) methods. The proposed methodology involves converting the multivariate time series to point cloud data, calculating Wasserstein distances between the persistence diagrams and using the k-nearest neighbors algorithm (k-NN) for supervised machine learning. Two methods (symmetry-breaking and anchor points) are also introduced to enable TDA to better analyze data with heterogeneous features that are sensitive to translation, rotation, or choice of coordinates. We apply our methods to room occupancy detection based on 5 time-dependent variables (temperature, humidity, light, CO2 and humidity ratio). Experimental results show that topological methods are effective in predicting room occupancy during a time window. We also apply our methods to an Activity Recognition dataset and obtained good results.