Gold price prediction using type-2 neuro-fuzzy modeling and ARIMA

In this research, gold price prediction is conducted using type-2 neuro-fuzzy modeling. Gold price data history is divided into several clusters using Self-Constructing Clustering and produces some type-2 fuzzy rules. The rules of fuzzy parameters which are preceding and consequent are sought and optimized using Particle Swarm Optimization and Least Square Estimation. The gold price prediction result using type-2 neuro-fuzzy modeling is compared to ARIMA method, which is a method that has been widely used for data prediction. The result from this experiment shows that the gold price prediction using type-2 neuro-fuzzy modeling has smaller error compared to the one obtained using ARIMA method.