In recent years, people are more and more interested in time series modeling and its application in prediction. This paper mainly discusses a financial time series image algorithm based on wavelet analysis and data fusion. In this research, we conducted an in-depth study on the scale decomposition sequence and wavelet transform sequence in different scale domains of wavelet transform according to the scale change rule based on wavelet transform. We use wavelet neural network with different input neurons and hidden neurons to predict, respectively. Finally, the prediction results are integrated into the final prediction results based on the original time series by using wavelet reconstruction technology. Using RBF algorithm in neural network and SPSS Clementine, the wavelet transform sequences on five scales are modeled. Each network model has three layers: one input layer, one hidden layer, and one output layer, and each output layer has only one output element. In order to compare the prediction effect of the model proposed in this study, the ordinary RBF network is used to model and predict the log yield itself. When the input sample is 5, the minimum mean square error is obtained when the hidden layer is 6, and the mean square error is 1.6349. The mean square error of the training phase is 0.0209, and the validation error is 1.6141. The results show that the prediction results of the wavelet prediction method combined with the RBF network prediction method are better than those of wavelet prediction or RBF network prediction.

中文翻译：

---

基于小波分析和数据融合的金融时间序列图像算法

近年来，人们对时间序列建模及其在预测中的应用越来越感兴趣。本文主要讨论基于小波分析和数据融合的金融时间序列图像算法。本研究根据小波变换的尺度变化规律，对小波变换不同尺度域的尺度分解序列和小波变换序列进行了深入研究。我们使用具有不同输入神经元和隐藏神经元的小波神经网络分别进行预测。最后，利用小波重构技术将预测结果整合到基于原始时间序列的最终预测结果中。利用神经网络中的RBF算法和SPSS Clementine，对五个尺度上的小波变换序列进行建模。每个网络模型都有三层：一个输入层，一个隐藏层和一个输出层，每个输出层只有一个输出元素。为了比较本研究提出的模型的预测效果，使用普通的RBF网络来建模和预测测井产量本身。当输入样本为5时，在隐藏层为6时获得最小均方误差，并且均方误差为1.6349。训练阶段的均方误差为0.0209，验证误差为1.6141。结果表明，小波预测方法与RBF网络预测方法相结合的预测结果优于小波预测或RBF网络预测。为了比较本研究提出的模型的预测效果，使用普通的RBF网络来建模和预测测井产量本身。当输入样本为5时，在隐藏层为6时获得最小均方误差，并且均方误差为1.6349。训练阶段的均方误差为0.0209，验证误差为1.6141。结果表明，小波预测方法与RBF网络预测方法相结合的预测结果优于小波预测或RBF网络预测。为了比较本研究提出的模型的预测效果，使用普通的RBF网络来建模和预测测井产量本身。当输入样本为5时，在隐藏层为6时获得最小均方误差，并且均方误差为1.6349。训练阶段的均方误差为0.0209，验证误差为1.6141。结果表明，小波预测方法与RBF网络预测方法相结合的预测结果优于小波预测或RBF网络预测。训练阶段的均方误差为0.0209，验证误差为1.6141。结果表明，小波预测方法与RBF网络预测方法相结合的预测结果优于小波预测或RBF网络预测。训练阶段的均方误差为0.0209，验证误差为1.6141。结果表明，小波预测方法与RBF网络预测方法相结合的预测结果优于小波预测或RBF网络预测。