To predict future stock prices using LSTM in Keras, you can follow these steps:
- Import the necessary libraries: Begin by importing the required libraries such as Keras, Numpy, Pandas, and Scikit-learn.
- Load the dataset: Obtain the historical stock price data of the desired stock from a reliable source. Read the dataset into a Pandas DataFrame.
- Preprocess the data: Preprocess the data to make it suitable for LSTM. Convert the stock prices to logarithmic values to handle non-stationary data and normalize the data using Min-Max scaling.
- Split the dataset: Divide the dataset into training and testing sets. Typically, 80% of the data is used for training and the remaining 20% for testing.
- Create the LSTM model: Design and create the LSTM model using Keras. Specify the number of LSTM units, dropout rate, and activation functions.
- Train the model: Compile and train the LSTM model using the training set. Set the appropriate loss function and optimizer.
- Make predictions: Predict the stock prices for the testing set using the trained LSTM model.
- Evaluate the model: Assess the performance of the model by calculating metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE).
- Visualize the predictions: Plot the actual stock prices against the predicted stock prices to visualize the accuracy of the model.
- Predict future stock prices: Use the trained LSTM model to predict future stock prices by providing the necessary input data. You can reshape the input data and use the model's predict function to obtain the predictions.
Note: LSTM models may not guarantee accurate predictions as stock prices are affected by various external factors. Therefore, it is essential to consider other factors like news, trends, and economic indicators for accurate predictions.