ArticleEntropy (Basel, Switzerland)2023
Topological Data Analysis for Multivariate Time Series Data.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Topological data analysis of postural sway: characterizing balance control during childhood development.Journal of neuroengineering and rehabilitation · 2026Article
- Methods for Brain Connectivity Analysis with Applications to Rat Local Field Potential Recordings.Entropy (Basel, Switzerland) · 2025Article
- Revealing brain network dynamics during the emotional state of suspense using TDA.Network neuroscience (Cambridge, Mass.) · 2025Article
- Article
- Robust Multi-Dimensional Time Series Forecasting.Entropy (Basel, Switzerland) · 2024Article
- Persistent homology reveals robustness loss in inhaled substance abuse rs-fMRI networks.PloS one · 2024Article
- Dynamic topological data analysis: a novel fractal dimension-based testing framework with application to brain signals.Frontiers in neuroinformatics · 2024Article
- Statistical inference for dependence networks in topological data analysis.Frontiers in artificial intelligence · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach that can deal with various data modalities of varying complexities. One of the most commonly used tools in TDA is persistent homology (PH), which can extract topological properties from data at various scales. The aim of this article is to introduce TDA concepts to a statistical audience and provide an approach to analyzing multivariate time series data. The application's focus will be on multivariate brain signals and brain connectivity networks. Finally, this paper concludes with an overview of some open problems and potential application of TDA to modeling directionality in a brain network, as well as the casting of TDA in the context of mixed effect models to capture variations in the topological properties of data collected from multiple subjects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.