Business Challenges

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

Suggested Solution

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 bein creased 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 transforms type, wavelet function, and the number of decomposition levels used in the MRA and, in that way, increases the prediction accuracy significantly