Intelligent Financial Modeling Data Analysis Based on MATLAB
Zesen Zhong · 2024
With the rapid growth and increased complexity of financial market data, traditional financial analytics models face challenges in handling large-scale datasets, real-time data analysis, and prediction accuracy. To overcome these limitations, this study employs a MATLAB-based long-short-term memory network (LSTM) model to compare with traditional financial models. Through the methods of data preprocessing, model construction and parameter optimization, the study finds that the LSTM model has significant advantages in forecasting accuracy and generalization ability. For example, on simulated financial time series data, the mean absolute error (MAE) of the LSTM model is 0.045, while that of the ARIMA model is 0.072. In addition, the LSTM achieves an accuracy of 95% on the test set, which is significantly better than that of the ARIMA model at 85%. These results suggest that although LSTM is slightly inferior in processing speed, its excellent performance makes it a powerful tool for financial data analysis. Future research could explore combining other deep learning techniques to further improve the performance and adaptability of the model.