Domestic previous research methods for this problem are mainly grey prediction model and Markov chain combination method (Qin Sheng and Chen Yang(2010)), BP neural network model (Gu Mengjun and Zhang Zhihe 2007)) and Grey Markoff model(Qin Sheng and Chen Yang(2010)) and ARFIMA model(Lin Yu (2010)) etc. Also, long-term regression model was applied to analysis the factors influencing the gold price which can give good results about the impact on the gold price mechanism (Bai Yichi); and Qian Bingbing(2007 has selected a fuzzy time series model to determine the init ial parameters of fuzzy system, while the use of a Type-2 and Type-1 fuzzy system. Fuzzy system is a good choice for training and prediction. To some extent, these models are accurate predictors of the gold price trend. For the international part, there are a plethora of methodological approaches. As with all methodologies, each is confronted with its own pros and cons. The methods range from qualitative methods (the most commonly used methods are judge mental forecasting, intensity of-use concept and the Delphi method); cost and reserve-based methods; trend extrapolation and time-series methods (e.g. Gocht et al., 1988); causal or behavioral models; to the use of futures markets for price forecasting (e.g. Roche, 1995). In terms of trend extrapolation and time-series methods, they attempt to forecast by extrapolating from past trends of prices. In other words, they empirically evaluate trends. Time-series methods are superior to trend extrapolation in their rigor and sophistication. However, ARIMA model provides marginally better forecast results than lagged forward price model (Gillian Dooley, Helena Lanihan 2005). Recursive and rolling neural network models are applied to forecast one-step-ahead sign variations in gold price.(Antonino Parisi, Franco Parisi, David Diaz 2008); A new method considers price jumps or dips in the models which does not separate mean reverting rate with jump time to forecast the price. This type of model contains slightly modified assumptions from the classical models.(Shahriar shafiee, Erkan Topal 2010). The forecasting for platinum, silver and gold prices using trader positions is investigated in a VAR framework. Granger causality tests are conducted to determine whether a relation between trader positions and market prices exists.(Takvor H.Mutafoglu a, EkinTokat b,n, HakkiA.Tokat c 2012).