Organisation: Vale – Cranfield University
Start and Estimated Duration: 28 – July – 2018, 24 Months
Summary: Metal price predictions support evaluations of future profits from metal exploration and mining and inform purchasing, selling and other day-to-day activities in the metals industry. Past research has shown that repeated behaviour is a dominant characteristic of metal prices. Wavelet analysis allows capturing this cyclicality by decomposing a time series into its frequency and time domain. This project assesses the usefulness of an improved combined wavelet-autoregressive integrated moving average (ARIMA) approach for predicting monthly prices of iron, aluminium, copper, lead and zinc. The performance of ARIMA models in forecasting metal prices is demonstrated to be increased significantly through a wavelet-based multiresolution analysis (MRA) before ARIMA model fitting. The method demonstrated in this project is an innovative approach because it identifies the optimal combination of the wavelet transform type; wavelet function and the number of decomposition levels used in the MRA and in that way increases the prediction accuracy significantly.
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