System Analysis and Forecast of Yield Time Series Based on Neural Network Technologies

Aubakirova G.F. Gerassimova Y.V. Ivel V.P. Arestenko T.V. Prymyska S.
April 2023 International Information and Engineering Technology Association

International Journal of Design and Nature and Ecodynamics
2023 #18 Issue 2 449 - 455 pp.

With the help of neural networks, it is possible to automate the processes of pattern recognition, adaptive control, forecasting, creating expert systems, etc. Neural networks can successfully solve problems that traditional methods cannot cope with, relying on incomplete, noisy or modified information. In its pure form, neural network modelling is based solely on data without using a priori theories. The North Kazakhstan region is the object of the study. The paper uses the theory of random processes and high-order Markov chains, allowing to build a model of a series; statistics and econometrics, which are used to select a plurality of lagged variables that affect predictive indicators, and to estimate the probability matrices of transitions between states; nonlinear optimisation methods to construct a nonlinear trend model with harmonic components aimed at predicting the low-frequency component of a time series. The artificial neural network will be implemented using Matlab. Studies have shown that the yield time series are persistent, that is, they have the effect of “long-term memory”. Autoregressive models cannot act as an adequate tool for modelling and forecasting such series. The practical significance of the study lies in the possibility to use the results to enhance plant yield at agricultural enterprises.

crops , difference series , neural networks , nonlinear trend , the least-squares method , vegetation index

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Department of Energy and Radioelectronics, M. Kozybayev North Kazakhstan University, 86 Pushkin Str., Petropavlovsk, 150000, Kazakhstan
Department of Marketing, Dmytro Motornyi Tavria State Agrotechnological University, 18 B. Khmelnitsky Ave., Melitopol, 72310, Ukraine
Department of Organic Chemistry and Technology of Organic Substances, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 37 Peremohy Ave., Kyiv, 03056, Ukraine

Department of Energy and Radioelectronics
Department of Marketing
Department of Organic Chemistry and Technology of Organic Substances

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