# A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks

arXiv: https://arxiv.org/abs/2107.08399 · HTML: https://mlsanity.com/paper/2107.08399/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Neural Network for Low-Memory IoT Devices and MNIST Image Recognition Using Kernels Based on Logistic Map](https://mlsanity.com/paper/2006.02824/) (2020)
- [Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network](https://mlsanity.com/paper/2105.02953/) (2021)
- [An improved LogNNet classifier for IoT application](https://mlsanity.com/paper/2105.14412/) (2021)

## Built on by (most cited first, 8 of 8)

- [Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing](https://mlsanity.com/paper/2210.06901/) (2022)
- [A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare](https://mlsanity.com/paper/2108.02428/) (2021)
- [Novel techniques for improving NNetEn entropy calculation for short and noisy time series](https://mlsanity.com/paper/2202.12703/) (2022)
- [Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network](https://mlsanity.com/paper/2205.09974/) (2022)
- [Machine Learning Sensors for Diagnosis of COVID-19 Disease Using Routine Blood Values for Internet of Things Application](https://mlsanity.com/paper/2209.03522/) (2022)
- [Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation](https://mlsanity.com/paper/2303.17995/) (2023)
- [A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science](https://mlsanity.com/paper/2306.01991/) (2023)
- [Solar Active Regions Detection Via 2D Circular Kernel Time Series Transformation, Entropy and Machine Learning Approach](https://mlsanity.com/paper/2306.08270/) (2023)

## Abstract (arXiv)

Measuring the predictability and complexity of time series using entropy is essential tool de-signing and controlling a nonlinear system. However, the existing methods have some drawbacks related to the strong dependence of entropy on the parameters of the methods. To overcome these difficulties, this study proposes a new method for estimating the entropy of a time series using the LogNNet neural network model. The LogNNet reservoir matrix is filled with time series elements according to our algorithm. The accuracy of the classification of images from the MNIST-10 database is considered as the entropy measure and denoted by NNetEn. The novelty of entropy calculation is that the time series is involved in mixing the input information in the res-ervoir. Greater complexity in the time series leads to a higher classification accuracy and higher NNetEn values. We introduce a new time series characteristic called time series learning inertia that determines the learning rate of the neural network. The robustness and efficiency of the method is verified on chaotic, periodic, random, binary, and constant time series. The comparison of NNetEn with other methods of entropy estimation demonstrates that our method is more robust and accurate and can be widely used in practice.