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http://dx.doi.org/10.9723/jksiis.2020.25.2.057

Forecasting of Iron Ore Prices using Machine Learning  

Lee, Woo Chang (계명대학교 경영정보학과)
Kim, Yang Sok (계명대학교 경영정보학과)
Kim, Jung Min (계명대학교 경영정보학과)
Lee, Choong Kwon (계명대학교 경영정보학과)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.25, no.2, 2020 , pp. 57-72 More about this Journal
Abstract
The price of iron ore has continued to fluctuate with high demand and supply from many countries and companies. In this business environment, forecasting the price of iron ore has become important. This study developed the machine learning model forecasting the price of iron ore a one month after the trading events. The forecasting model used distributed lag model and deep learning models such as MLP (Multi-layer perceptron), RNN (Recurrent neural network) and LSTM (Long short-term memory). According to the results of comparing individual models through metrics, LSTM showed the lowest predictive error. Also, as a result of comparing the models using the ensemble technique, the distributed lag and LSTM ensemble model showed the lowest prediction.
Keywords
Machine learning; Price of iron ore; Granger causality; Time-series forecasting;
Citations & Related Records
Times Cited By KSCI : 13  (Citation Analysis)
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