Enhanced Online LS-SVR using EMD for Prices Prediction of Copper

Abstract

The cost estimate is an important issue faced by all manufacturers before starting off a project The major task is to predict the future price of the material in order to control inventory costs Despite the prediction system based on support vector machine (SVM) has recently become a good solution the accuracy of the predicted values are usually deteriorated with non-stationary price data Therefore to further explore an effective and feasible method of price prediction is still a huge challenge in materials’ cost control In this thesis least squares support vector regression (LS-SVR) is proposed to predict the trend of copper price The design combines empirical mode decomposition (EMD) to decompose nonlinear and non-stationary data into several intrinsic mode function (IMF) components and one residual component Therefore better prediction can be attained by forecasting these IMFs and residual value individually with corresponding online LS-SVR According to our test results proposed design improves prediction accuracy from online LS-SVM for the trend of copper price In addition we discuss the relationship between copper gold platinum silver West Texas Intermediate (WTI) GBP/USD exchange rate and inventory by the long-term trend of the sum of few IMFs and residue

Date of Award	2014 Jul 16
Original language	English
Supervisor	Pei-Yin Chen (Supervisor)