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

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 le

SUBMITTER: Velichko A 

PROVIDER: S-EPMC8621949 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature