Intelligent System Using Support Vector Regression for Demand Forecasting

ABSTRACT: Supply chain management (SCM) is an emerging field that has commanded attention from different communities. On the one hand, the optimization of supply chain which is an important issue, requires a reliable prediction of future demand. On the other hand, It has been shown that intelligent systems and machine learning techniques are useful for forecasting in several applied domains. In this paper, they used the Particle Swarm Optimization (PSO) algorithm to optimize the SVR parameters. Furthermore, we will use the Artificial Bee Colony (ABC) algorithm to optimize the SVR parameters. Furthermore, We will investigate the time complexity of SVRPSO and SVR-ABC for supply chain demand forecasting by comparing these two algorithms.