Financial Market Sequence Prediction Based on Image Processing

Abstract

The rapid development of image and video processing technology has a significant impact on various industries. The analysis and forecast of the financial market income can not only provide investors with investment decisions, but also can correctly formulate various economic control policies for the government. The purpose of this study is to analyze and predict the financial market returns and various indexes based on deep learning CNN neural network algorithm in image processing technology. This study uses the time series method, using the convolution pooling process in CNN to effectively capture the local correlation characteristics of financial market data, then extract the important information hidden in the time series data, draw the trend curve of this information, and combine the features through image processing technology, finally realize the prediction of the financial market time series income index. The results show that in the deep learning algorithm of this study, the highest actual value of stock price after image processing is 3374, and the highest error value is 5.176%, which is nearly 20% less than other algorithms. When N1 = 1600, 3032 sliding windows are obtained, and the Euclidean norm of this point is 0.1586. The conclusion is that the deep learning algorithm of this study is effective and accurate for the prediction of financial market series. Image processing and data analysis technology provide effective methods and make important contributions to the research of financial field.