Due to the characteristics of financial time series, such as being non-linear, non-stationary and noisy, with uncertain and hidden relationships, it is difficult to capture its non-stationary state and to accurately describe its moving tendency. This is also a consequence of using a single approach to artificial intelligence, and other techniques that have been conventionally used. Therefore, those participating in financial markets, along with researchers, have paid a great deal of attention to tackling this problem. Hence, several approaches have been developed to alleviate the influence of inherent characteristics. However, the noise characteristic can refer to the unavailability of information, which affects how financial markets behave, as well as captured prices in both the past and the future. Therefore, the prediction of stock prices and detecting their noise is considered a very challenging financial topic. This paper adopts a novel three-step hybrid intelligent prediction model that combines a collection of intelligent modelling techniques and a feature extraction algorithm. At first, ensemble empirical mode decomposition is applied to the original data, as to facilitate model fitting to them. Then neural network and support vector regression is proposed individually for modelling the extracted features. Finally, a weighted ensemble average using a genetic algorithm to optimise and determine the weight is proposed for establishing a unified prediction. Experimental results are presented which illustrate the excellent performance of the proposed approach, and that is significantly outperforming the existing models, in terms of error criteria such as MSE, RMSE and MAE.

由于金融时间序列具有非线性，不稳定和嘈杂的特点，具有不确定性和隐性关系，因此难以捕捉其不稳定状态并准确描述其移动趋势。这也是使用单一人工智能方法和其他常规使用技术的结果。因此，参与金融市场的人们以及研究人员对解决这一问题给予了极大的关注。因此，已经开发出几种方法来减轻固有特性的影响。但是，噪声特征可以表示信息的不可用性，这会影响金融市场的行为方式以及过去和将来的价格捕获。因此，预测股票价格并检测其噪音被认为是非常具有挑战性的财务主题。本文采用了一种新颖的三步混合智能预测模型，该模型结合了智能建模技术和特征提取算法的集合。首先，将集成经验模式分解应用于原始数据，以利于对其进行模型拟合。然后分别提出了神经网络和支持向量回归来对提取的特征进行建模。最后，提出了一种利用遗传算法优化和确定权重的加权综合平均数，以建立统一的预测方法。实验结果表明，该方法具有出色的性能，并且明显优于现有模型，