Developing a Time Series Model Based on Particle Swarm Optimization for Gold Price Forecasting

The trend of gold price in the market is the most important consideration for the investors of the gold, and serves as the basis of gaining profit, so there are scholars who try to forecast the gold price. Forecasting accuracy is one of the most important factors involved in selecting a forecasting method. Besides, nowadays artificial intelligence (AI) techniques are becoming more and more widespread because of their accuracy, symbolic reasoning, flexibility and explanation capabilities. Among these techniques, particle swarm optimization (PSO) is one of the best AI techniques for optimization and parameter estimation. In this study a PSO-based time series model for the gold price forecasting is proposed that uses PSO algorithm for parameter estimation. We evaluate capability of the proposed model by applying it on daily observation of gold price and compare the outcomes with previous methods using mean absolute error (MAE). Results show that the proposed approach is able to cope with the fluctuations of gold price time series and it also yields good prediction accuracy, so it can be considered as a suitable tool for financial forecasting problems.