Potential Demand Forecasting for Steel Products in Spot Markets Using a Hybrid SARIMA-LSSVM Approach

Abstract

Compared to make-to-order production based on customer order, make-to-stock based on forecast can effectively reduce inventory level and production cost. However, due to high randomness of spot markets and many uncertainties in production environments, it is hard to forecast the products accurately. In this article, a hybrid model combining seasonal autoregressive integrated moving average (SARIMA) and least square support vector machines (LSSVMs) is proposed to forecast the potential demand of steel products. First, the SARIMA based on a multiobjective differential evolution with improved mutation strategies is developed to extract linear components of the potential demand. Then, a sparse strategy is designed to extract useful data and hence reduce computation complexity without loss of accuracy. Next, the LSSVMs combined with a single-objective differential evolution are adopted to extract nonlinear components of the potential demand. Finally, the experimental results on a real-world instance demonstrate the effectiveness of the proposed model and algorithm.