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Forecasting the Dynamics of Stockbreeding Development Using Time Series
Kulyk A. B.

Kulyk, Anatolii B. (2024) “Forecasting the Dynamics of Stockbreeding Development Using Time Series.” Business Inform 1:110–117.
https://doi.org/10.32983/2222-4459-2024-1-110-117

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
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UDC 338.432

Abstract:
The construction of time series using historical data is one of the urgent problems of management in the agrarian sector, since the analysis and forecasting of processes related to the food security of the State, of region, and of economic entities is crucial. With the help of forecasts, businesses can adjust their production activities in such a way as to meet demand and deliver products to consumers on time. The aim of this study is to forecast the dynamics of the development of stock of cattle and cows and to determine the optimal forecasting period. For this type of analysis, statistical methods related to autoregression are used: autoregressive models, moving average models, or combinations of both; integrated models with a variable structure, and models that include seasonal effects and exogenous factors with an autoregressive and moving average component in the model. Monthly statistics on the number of cattle and cows are provided: mean, standard deviation, minimum and maximum values, asymmetry and excess. The dynamics of decline in the number of stock of cattle and cows is shown. The studied series were tested for stationarity. To the time series of the number of cattle, the Box–Cox transformation was applied. The optimal parameters of the models used are presented. Forecast values for time intervals (months) have been obtained and changes in the number of stock of cattle over the past 17 years have been analyzed. The built time series are compared with the actual values, which is illustrated in the graphs. Estimates of standard deviation, mean absolute error for different forecasting terms are provided. When comparing these estimates for different time intervals, the optimal time period for the forecast (24 months) was determined. This study allows farms and enterprises of the industry to realize how much products (milk, meat) can be harvested or obtained in the future. This assists in taking the necessary managerial steps: plan for resource needs, improve efficiency, increase profits, reduce costs, and adapt to changes in the market.

Keywords: stockbreeding, time series, forecasting, Box–Cox transformation.

Fig.: 2. Tabl.: 5. Formulae: 3. Bibl.: 16.

Kulyk Anatolii B. – Candidate of Sciences (Physics and Mathematics), Associate Professor, Head of the Department, Department of Higher Mathematics, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]

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