Optimization of financial data processing strategy based on LSTM-GCN

Abstract

In the process of training LSTM-GCN mixed model with labeled data, a common practice is to divide the data set into a training set and a validation set, and cross-validation techniques are frequently used to ensure the effective partitioning of data. The training phase mainly involves the selection of optimization methods such as gradient descent to update the model parameters, so that the model can correctly identify the feature patterns in the data. In this process, we first define a loss function to quantify the difference between the model's predicted value and the actual value. For example, when dealing with classification problems, the cross-entropy loss function is often used; Whereas in regression problems, the mean square error loss function or the absolute error loss function is more common. Then, the loss gradient of each parameter is calculated by backpropagation algorithm, and optimization strategies such as gradient descent are applied to adjust the parameter values. Through repeated iterations, these algorithms aim to minimize the loss function as a core goal for training the model. In addition, the details of model training, such as setting the appropriate learning rate, taking the appropriate regularization measures, and choosing the appropriate weight initialization strategy, are also key steps that cannot be ignored. This paper conforms to the research and development trend of the industry, focusing on how to use the LSTM-GCN model to capture temporal and spatial characteristics, process multi-modal data, process nonlinear data and dynamic weight allocation, etc., providing new ideas for Internet finance companies in data processing. This model combines the advantages of LSTM and GCN, and has prominent advantages such as timeliness, strong representation learning ability, associability, and interpretability. It can process both time series data and graphic data, and can analyze and mine the correlation and regularity in large-scale time series data, so as to process financial data more accurately. It greatly reduces the risk of investment decision.