# Homological Time Series Analysis of Sensor Signals from Power Plants

arXiv: https://arxiv.org/abs/2106.02493 · HTML: https://mlsanity.com/paper/2106.02493/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [Searching for Activation Functions](https://mlsanity.com/paper/1710.05941/) (2017)
- [giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Exploration](https://mlsanity.com/paper/2004.02551/) (2020)
- [Stochastic Convergence of Persistence Landscapes and Silhouettes](https://mlsanity.com/paper/1312.0308/) (2013)
- [Topological Time Series Analysis](https://mlsanity.com/paper/1812.05143/) (2018)
- [State Space Representations of Deep Neural Networks](https://mlsanity.com/paper/1806.03751/) (2018)
- [Persistent Homology as Stopping-Criterion for Voronoi Interpolation](https://mlsanity.com/paper/1911.02922/) (2019)
- [Estimate of the Neural Network Dimension using Algebraic Topology and Lie Theory](https://mlsanity.com/paper/2004.02881/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning Transfers: Kan Extensions of Topological Invariants of Learned Representations](https://mlsanity.com/paper/2606.07627/) (2026)

## Abstract (arXiv)

In this paper, we use topological data analysis techniques to construct a suitable neural network classifier for the task of learning sensor signals of entire power plants according to their reference designation system. We use representations of persistence diagrams to derive necessary preprocessing steps and visualize the large amounts of data. We derive deep architectures with one-dimensional convolutional layers combined with stacked long short-term memories as residual networks suitable for processing the persistence features. We combine three separate sub-networks, obtaining as input the time series itself and a representation of the persistent homology for the zeroth and first dimension. We give a mathematical derivation for most of the used hyper-parameters. For validation, numerical experiments were performed with sensor data from four power plants of the same construction type.