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Using multi-stage data mining technique to build forecast model for Taiwan stocks

机译：利用多阶段数据挖掘技术建立台湾股票预测模型

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摘要

Taiwan stock market trend is fast changing. It is affected by not only the individual investors and the three major institutional investors, but also impacted by domestic political and economic situations. Therefore, to precisely grasp the stock market movement, one must build a perfect stock forecast model. In this article, we used a multi-stage optimized stock forecast model to grasp the changing trend of the stock market. First, data of 2 stocks, TSMC and UMC were collected, and then inputted the test data into the genetic programing and built a model to find out the arithmetic expressions. Artificial Fish Swarm Algorithm is used to dynamically adjust the variable factors and constant factors in the arithmetic expressions. Next, we took the error term (ε) in arithmetic expressions to Gray Model Neural Network to make the forecast. Finally, we used the Artificial Fish Swarm Algorithm to dynamically adjust the parameters of the Gray Model Neural Network to enhance the precision of the stock forecast model as a whole. The result showed that the forecast capability of each stage after the optimization process is better than that of its previous stage, and the mixed stock forecast model (GP-AFSA+GMNN-AFSA) in stage 4 greatly enhanced the precision of the forecast.
机译：台湾股市趋势瞬息万变。它不仅受到个人投资者和三大主要机构投资者的影响，而且还受到国内政治和经济形势的影响。因此，要精确地掌握股市走势，就必须建立一个完善的股票预测模型。在本文中，我们使用了多阶段优化的股票预测模型来掌握股票市场的变化趋势。首先收集台积电，联华电子等2只股票的数据，然后将测试数据输入遗传程序，建立模型以求出算术表达式。人工鱼群算法用于动态调整算术表达式中的变量因子和常数因子。接下来，我们将算术表达式中的误差项（ε）带入Gray Model Neural Network进行预测。最后，我们使用人工鱼群算法动态调整灰色模型神经网络的参数，以提高整个种群预测模型的精度。结果表明，优化后各阶段的预测能力要优于前阶段，第4阶段的混合库存预测模型（GP-AFSA + GMNN-AFSA）大大提高了预测的准确性。

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