Title: An Enhanced Wavelet–ARIMA Method for Predicting Metal Prices
Organization: Vale – Cranfield University
Start and Estimated Duration : 28, July 2018 - 24 Months
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 behavior 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, aluminum, 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.