Building a Long Short-Term Memory (LSTM) Network for Stock Market Prediction

Welcome to this tutorial on building a Long Short-Term Memory (LSTM) network for stock market prediction. In this tutorial, we will explore the concept of LSTM networks and how they can be used to predict stock market prices. We will start by understanding the basics of LSTM networks and then move on to implementing them in Python using the Keras library.

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Table of Contents

1. Introduction to LSTM Networks
2. Understanding Stock Market Data
3. Preparing the Data
4. Building the LSTM Model
5. Training the LSTM Model
6. Evaluating the Model
7. Making Predictions
8. Conclusion

1. Introduction to LSTM Networks

LSTM networks are a type of recurrent neural network (RNN) that are particularly effective in handling sequence data. They are widely used in various applications such as natural language processing, speech recognition, and time series analysis. LSTM networks are capable of learning long-term dependencies in data, making them well-suited for predicting stock market prices.