Combined modelling for iron ore demand forecasting with intelligent optimization algorithms

• Published in: Gospodarka surowcami mineralnymi Vol. 37; no. 1; p. 21
• Main Authors: Ren, Min, Dai, Jianyong, Zhu, Wancheng, Dai, Feng
• Format: Journal Article
• Language: English
• Published: Warsaw Polish Academy of Sciences 01.01.2021
• Subjects: Algorithms; Artificial neural networks; Autoregressive models; Economic forecasting; Energy security; Forecasting; Genetic algorithms; Iron; Iron compounds; Iron ores; Learning algorithms; Machine learning; Model accuracy; Optimization algorithms; Particle swarm optimization; Prediction models; Simulated annealing; Statistical analysis; Support vector machines; Sustainable development
• ISSN: 0860-0953; 2299-2324
• DOI: 10.24425/gsm.2021.136293

The stable supply of iron ore resources is not only related to energy security, but also to a country’s sustainable development. The accurate forecast of iron ore demand is of great significance to the industrialization development of a country and even the world. Researchers have not yet reached a...