Forecasting steel production and world trade balance of ores and metals using the ARIMA method
Abstract
Steel is one of the most important raw materials used in almost all spheres of our life, directly or indirectly affecting the industry and economy of the country. This article investigates the forecasting of global steel production, exports and imports of ore and metals of all countries of the world and Russia separately using the ARIMA (Autoregressive Integrated Moving Average) method based on data for the period from 2000 to 2020. The ARIMA method is used for time series modeling, taking into account autocorrelation, trends and seasonality of data. Forecasts obtained using this method provide valuable results for strategic decision making in the steel industry and global trade. The forecast for 15 years ahead shows a significant increase in the steel production. World ore and metal exports, reflecting the dependence of exports on ore and metal production and the specialization of economies in this area, are projected to grow by almost twice as much as in 2020. The global ore and metal imports indicator shows import growth over the next 15 years. For Russia, the analysis also shows a decrease in the share of imports of ore and metals in the total volume of goods and preservation of the share of exports at the current level.
About the Author
L. D. Savenkov
Institute of Finance, Economics and Management of Togliatti State University
Russian Federation
Russian Federation
Leonid D. Savenkov – PhD in Economics, Associate Professor
Togliatti
References
1. Pourmehdi M., Paydar M.M., Asadi-Gangraj E. Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology // Journal of Cleaner Production. 2020. No. 277. doi:10.1016/j.jclepro.2020.123298.
2. A country-level multi-objective optimization model for a sustainable steel supply chain / B.L. Condé, J.F. de F. Almeida, D.M. Miranda, S.V. Conceição // Exacta. 2024. doi:10.5585/2024.22996.
3. Proposing an agile strategy for a steel industry supply chain through the integration of balance scorecard and Interpretive Structural Modeling / A. Tizroo, A. Esmaeili, E. Khaksar, J. Šaparauskas, M.M. Mozaffari // Journal of Business Economics and Management. 2017. No. 18 (2). doi:10.3846/16111699.2017.1279683.
4. Borji M.K., Sayadi A.R., Nikbakhsh E. A Novel Sustainable Multi-objective Optimization Model for Steel Supply Chain Design Considering Technical and Managerial Issues: a Case Study // Journal of Mining and Environment. 2023. No. 14 (1). doi:10.22044/jme.2023.12556.2280.
5. A systems dynamics simulation model of a steel supply chain-case study / M.A. Mohammadi, A.R. Sayadi, M. Khoshfarman, A. Husseinzadeh Kashan // Resources Policy. 2022. No. 77. doi:10.1016/j.resourpol.2022.102690.
6. Khoza S., Mafini C., Okoumba W.V.L. Lean practices and supply-chain competitiveness in the steel industry in Gauteng, South Africa // South African Journal of Economic and Management Sciences. 2022. No. 25 (1). doi:10.4102/sajems.v25i1.4617.
7. Towards defossilised steel: Supply chain options for a green European steel industry / G. Lopez, T. Galimova, M. Fasihi, D. Bogdanov, C. Breyer // Energy. 2023. No. 273. doi:10.1016/j.energy.2023.127236.
8. Albeladi K., Zafar B., Mueen A. Time Series Forecasting using LSTM and ARIMA // International Journal of Advanced Computer Science and Applications. 2023. No. 14 (1). doi:10.14569/IJACSA.2023.0140133.
9. Sirisha U.M., Belavagi M.C., Attigeri G. Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison // IEEE Access. 2022. No. 10. doi:10.1109/ACCESS.2022.3224938.
Review
For citations:
Savenkov L.D. Forecasting steel production and world trade balance of ores and metals using the ARIMA method. Vestnik of Samara State University of Economics. 2024;(7):37-43. (In Russ.)
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