An Improved Forecasting Algorithm for Spare Parts of Short Life Cycle Products Based on EMD-SVM

Abstract

Demand of spare parts of short life cycle products has great random fluctuation and short life cycle. Traditional forecasting methods have low forecasting accuracy which leads to under stock or overstock of spare parts. Considering such situation an improved forecasting method based on Empirical Mode Decomposition and Support Vector Machine (IEMD-SVM) is proposed. By replacing the Cubic Spline Interpolation in the standard EMD with Piecewise Cubic Hermite Interpolation, the overshoots and undershoots problems caused by great volatility of data are solved. Experiments with 459 real data sets show that the IEMD-SVM forecasting method has a better forecasting result than traditional forecasting methods which provides better decision supports for enterprise inventory management.