Title :
Gold price prediction using type-2 neuro-fuzzy modeling and ARIMA
Author :
Christina, Chintya ; Umbara, Rian Febrian
Author_Institution :
Sch. of Comput., Telkom Univ., Bandung, Indonesia
Abstract :
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.
Keywords :
financial data processing; fuzzy neural nets; gold; least mean squares methods; particle swarm optimisation; pattern clustering; pricing; ARIMA; fuzzy parameters; gold price data history; gold price prediction; least square estimation; particle swarm optimization; self-constructing clustering; type-2 fuzzy rules; type-2 neuro-fuzzy modeling; Data models; Forecasting; Gold; Predictive models; System analysis and design; Testing; Training data; ARIMA; Type-2 Neuro-Fuzzy Modeling; gold price prediction;
Conference_Titel :
Information and Communication Technology (ICoICT ), 2015 3rd International Conference on
Conference_Location :
Nusa Dua
DOI :
10.1109/ICoICT.2015.7231435
