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Preprocessing of statistical data to improve the quality of the forecast by a neural network

Gilmanov A. R.  (Perm State National Research University)

Gusev A. L.  (Perm State National Research University)

Okunev A. A.  (Perm State National Research university)

The article describes the method of functional preprocessing of statistical data to improve the forecast obtained with the help of neural networks. We consider a fairly wide range of functions that can be used to pre-process statistical data. The advantage of neural networks for forecasting using data preprocessing is shown, in terms of forecast stability. The forecast error is considered as a random variable for which: statistical estimates for the mathematical expectation and for the standard deviation are calculated, and a selective coefficient of variation is calculated to determine the most stable forecast model.

Keywords:functional preprocessing, forecast, stability of the neural network model, coefficient of variation.
 

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Citation link:
Gilmanov A. R., Gusev A. L., Okunev A. A. Preprocessing of statistical data to improve the quality of the forecast by a neural network // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2018. -№03. -С. 49-51
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