ArticleEntropy (Basel, Switzerland)2021
A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Kalman filtering to reduce measurement noise of sample entropy: An electroencephalographic study.PloS one · 2024Article
- A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application for Computational Neuro-Science.Sensors (Basel, Switzerland) · 2023Article
- A new hybrid prediction model of COVID-19 daily new case data.Engineering applications of artificial intelligence · 2023Article
- Entropy of Financial Time Series Due to the Shock of War.Entropy (Basel, Switzerland) · 2023Article
- Collective excitations of germinating pollen grains at critical points.Scientific reports · 2023Article
- Bifurcation and Entropy Analysis of a Chaotic Spike Oscillator Circuit Based on the S-Switch.Entropy (Basel, Switzerland) · 2022Article
- Diagnosis and Prognosis of COVID-19 Disease Using Routine Blood Values and LogNNet Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Entropy Method of Road Safety Management: Case Study of the Russian Federation.Entropy (Basel, Switzerland) · 2022Article
- A Method for Medical Data Analysis Using the LogNNet for Clinical Decision Support Systems and Edge Computing in Healthcare.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
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Abstract
Measuring the predictability and complexity of time series using entropy is essential tool designing 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 reservoir. 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.
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