Applications of Deep Learning in Financial Time Series Forecasting

With the increasing volatility and complexity of the financial market, the traditional time series forecasting methods often face the challenges of accuracy and stability in processing financial data. In recent years, the application of deep learning technology in financial forecasting has received extensive attention, especially the convolutional neural network (CNN), long-term and short-term memory network (LSTM), time convolutional network (TCN) and other models. The paper first introduces the theory of deep learning management. Secondly, it introduces the application of deep learning in financial time series prediction. Finally, according to the experimental results, the effectiveness of the model in improving the prediction accuracy and dealing with uncertainty is verified.