The article begins by introducing the concept of LSTM networks and the attention mechanism, highlighting their relevance in financial modeling and stock price prediction. It then covers the process of setting up the coding environment in Google Colab, including the installation of necessary libraries such as TensorFlow, Keras, and yfinance.
The data preprocessing and preparation section emphasizes the importance of handling missing values, normalizing the data, and creating sequences suitable for the LSTM model. The author then delves into the construction of the LSTM model with an attention mechanism, explaining the various layers and the integration of the attention mechanism.
The training process is discussed, including techniques to avoid overfitting, such as using a validation set, implementing early stopping, and incorporating regularization methods like Dropout and Batch Normalization. The article then evaluates the model's performance using the test set, calculating metrics like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to assess the model's accuracy.
Finally, the guide demonstrates how to use the trained model to predict the next four candles of AAPL stock prices, providing a step-by-step approach. The author also includes a visualization of the actual and predicted stock prices, allowing for a comprehensive understanding of the model's performance.
Throughout the article, the author emphasizes the importance of continuous exploration and experimentation to refine and adapt these methods for more sophisticated applications in the financial markets.
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by Dr. Ernesto ... at drlee.io 04-08-2024
https://drlee.io/advanced-stock-pattern-prediction-using-lstm-with-the-attention-mechanism-in-tensorflow-a-step-by-143a2e8b0e95Deeper Inquiries