Stock trend prediction method and device based on sequence-to-graph

• Main Authors: TANG WEIJIA, WU YADONG, SUN KEWEI, ZHANG HUA
• Format: Patent
• Language: Chinese; English
• Published: 11.04.2023
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention provides a sequence-to-graph-based stock trend prediction method and device, and relates to the technical field of finance, and the method comprises the steps: obtaining time sequence data in stock transaction historical data; sampling from the time sequence data to obtain technical index sequence data of the stock; converting the technical index sequence data into a two-dimensional image containing spatial-temporal characteristics by using a horizontal propagation method; and taking the two-dimensional image as the input of a convolutional neural network classification model, and outputting the two-dimensional image after convolution operation to obtain a stock trend classification result. According to the method, the technical index time sequence data is converted into the graph, on one hand, the calculation efficiency of the model can be effectively improved, on the other hand, stock trend information can be learned in the space and time, and the trend judgment result is more accurate. In add