Stock prediction based on convolutional neural network combined with long-term and short-term memory neural network

This paper analyzes the predictive performance of convolutional neural networks and long short-term memory (LSTM) neural networks on Amazon stock data and identifies prospects for implementing deep learning methods in stock forecasting. By analyzing various dimensions of the Amazon dataset and using the PyTorch architecture to model convolutional neural networks and LSTM neural networks, challenges in improving their structures are identified. This study specifically focuses on the internal structures of convolutional neural networks and LSTM neural networks, as well as their advantages and disadvantages in stock forecasting. Convolutional neural networks (CNN) perform well in extracting local features, such as short-term trends and fluctuation characteristics, from stock data. However, there are still challenges such as an inability to focus on long-term trends and insensitivity to sequencing data. Long short-term memory (LSTM) networks perform well on time series data and can effectively capture long-term dependencies in inventory data. However, it is insensitive to local features, and in the presence of a large amount of noise, it will overfit the training data, resulting in insufficient generalization. Therefore, this paper develops a CNN-LSTM model that combines the advantages of both models. The CNN is responsible for extracting local features, while the LSTM is responsible for capturing long-term trends and cyclical patterns, enabling more comprehensive feature extraction and time-series data modeling. The results show that the CNN-LSTM model outperforms either the CNN or LSTM models in stock price forecasting and is better able to handle nonlinearities and complex market fluctuations.