CNN-LSTM Stock Price Prediction Based on Bayesian Network

CNN-LSTM Stock Price Prediction Based on Bayesian Network

In the financial field, stock price prediction has always been a much concerned and challenging problem. Traditional methods usually use time series models, but these models often cannot take into account the complex relationship between related factors. This paper proposes a hybrid model based on Bayesian Networks, CNN, and LSTM aimed at predicting stock prices more accurately.

The role of Bayesian network is to capture the dependencies between relevant factors, while CNN can effectively extract feature information in time series data. LSTMs are suitable for dealing with long-term dependencies and nonlinear relationships. Therefore, the whole model organically combines Bayesian network, CNN and LSTM.

Here are the main steps of the model:

1. Data Collection: Obtain historical data of Apple Inc (AAPL) from Yahoo Finance, including daily opening, highest, lowest, closing and trading volume.

2. Data preprocessing: Transform raw data into smooth time series data through logarithmic transformation, normalization and difference processing.

3. Bayesian network modeling: Using Python's Bayesian Networks library, a Bayesian network model was built on the processed time series data.

4. CNN-LSTM model establishment: the Bayesian network structure and the CNN-LSTM framework are combined, the input is the previously processed time series data, and the output is the stock price of the next day.

5. Train and Test: Use maximum likelihood estimation to train the model and compare the results on the test set with the ground truth. The performance of the model was evaluated by mean square error and mean absolute error.

Here is the complete Python code:

# 导入必要的库
import pandas as