ИСПОЛЬЗОВАНИЕ МОДЕЛИ МНОЖЕСТВЕННОЙ ЛИНЕЙНОЙ РЕГРЕССИИ ДЛЯ КРАТКОСРОЧНОГО ПРОГНОЗИРОВАНИЯ ЦЕН НА КАЛИЙНУЮ ПРОДУКЦИЮ

Abstract

The modern economy instability due to global and regional conflicts and contradictions leads to significant fluctuations in commodity markets. The demand and prices unpredictability leads to the raw materials in-dustries risks increase and limits their successful functioning and development. The purpose of the study. In this paper, we made an attempt of a multiple linear regression model application to obtain a forecast price of potash products acceptable quality. Despite the wide application and simplicity of construction and interpretation, the forecasting properties of such models are usually unsatisfactory. Nevertheless, an adequate selection of factors and the sample size used to estimate unknown parameters of the model, al-lows to achieve an acceptable forecasting quality. Materials and methods. In the issue the evaluation of the multiple linear regression model unknown parameters is performed on the basis of United States of America Geological Survey data. The sample contains information about the American potash market in the period from 1900 to 2020. The quality of forecasting is tested using the post-forecast method for years 2019 and 2020. The model built using the entire dataset gives an unsatisfactory relative prediction error. To reduce its value, a search of the sample volumes of data used for model parameters estimation was per-formed, and the one with the minimal forecast error was selected as optimal. The calculations were per-formed using MS Excel and Jupyter Notebook 6.1.4 environment for Python 3.8.5. Results. The conducted research allowed us to determine that in order to minimize the US domestic market potash products price relative error, it is necessary to use from 9 to 13 years time intervals for modeling. By doing that it is possi-ble to improve the 118-year time interval forecast for 2019 year by 23.9%, and a similar forecast for 2020 by 83.70%. Conclusion. Based on the results of the work done, it can be stated that the multiple linear re-gression model can be successfully used for short-term forecasting of the potash products price, and by an adequate modeling interval length choice it is possible to achieve acceptable forecasting quality.