Abstract
The article proposes a method for constructing and correcting a multistep forecast for the year ahead (with a monthly breakdown) of prices for raw materials and products of industrial enterprises. The proposed approach consists in the formation of a price forecast taking into account 1) the price of the predicted indicator, the prices of goods participating in the product value chain, and macro indicators (time series); 2) information about the strength and direction of environmental factors affecting the market. Structured information about the effects of the external environment is the result of processing expert knowledge and hypotheses from heterogeneous information sources, through analysis and modeling on a cognitive map of the situation (CCS). We form a forecast by constructing an ensemble of time series models, each of which reflects the dependence of the target indicator on its past values and the prices of related products, the composition of which is determined by the results of cognitive modeling and time series analysis. Based on the results of monitoring on the cognitive map of the situation, conducting in order to analyze possible changes in the external environment and digital monitoring of prices, to identify changes in prices modes, we perform a forecast correction. The results obtained in this study show that the use of cognitive modeling and monitoring of changes improve the accuracy of predictions.
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Avdeeva, Z.K., Grebenyuk, E.A., Kovriga, S.V. (2021). Construction of Multi-step Price Forecasts in Commodity Markets Based on Qualitative and Quantitative Data Analysis Methods. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-030-85874-2_68
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DOI: https://doi.org/10.1007/978-3-030-85874-2_68
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