A mapping chase autoregression shape is applied to predict gold worth here. Previous works centered on prediction of the instability of gold worth to reveal the characteristics of gold market. By the way, due to the fact that the data of gold worth have high dimensionality, MCAF is suitable and able to predict gold worth more accurately rather than other mechanisms. In this paper, the MCAF is used to the everyday worth of gold. The experimental results indicate MCAF outperforms BPNN technique, especially on stability, which reveals the advantage of MCAF technique in dealing with huge amounts of data.
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