Forecasting the selling price of the agricultural products in Ukraine using deep learning algorithms

Abstract

The paper aims to figure out the effectiveness of machine learning algorithms in the price forecasting of agricultural products based on the example of barley prices. In addition, the article provides a comparative analysis of traditional forecasting methods and deep learning algorithms, and also considers the expediency of their use in enterprises and in public administration. The authors use time series forecasting methods and models, in particular, traditional prediction methods (Linear Regression and Fb Prophet) and different strategies of deep learning algorithms (recursive multi-step and Direct-recursive hybrid convolutional neural networks) were used. As a result, the study shows that traditional methods and neural networks show sufficiently greater results than naive forecasts; however, at the same time, traditional models are more effective than deep learning models, and they require less time and fewer resources to implement. It has been established that neural networks, in contrast to traditional forecasting methods, take into account other patterns, so it makes sense to consider the possibility of using neural networks together with traditional forecasting methods using ensemble methods. The article considers the conditions under which it is advisable to use methods in enterprises, as well as in public regulation. Hence, results of the study can be used in the following ways: a) in research activities in the agricultural sector; b) practically in the planning process in enterprises of the agricultural sector; c) companies related to the above industry, such as logistics companies or financial enterprises; 4) in public planning, budgeting and control.