Predict Stock Prices with LSTM Networks

Contributed by: Brindha Priyadarshini Jeyaraman

Difficulty level: Advanced

In this Shortcut, you’ll learn how to use Long Short-Term Memory (LSTM) networks to predict stock prices. By the end, you’ll understand how neural networks can model complex time series data, enabling you to make informed investment decisions. LSTM networks are designed to capture long-term dependencies, making them ideal for forecasting stock prices.

Introducing LSTM Networks

LSTM networks are a type of recurrent neural network (RNN) capable of learning long-term dependencies. They are particularly effective for time series prediction tasks due to their ability to retain information for long periods.

Traditional RNNs only have short-term memory and are, therefore, unable to carry forward information for long sequences due to the vanishing gradient problem. LSTMs address this issue with special units called memory cells that can maintain their state over time, controlled by gates that regulate the flow of information.

Steps to Predicting Stock Prices with LSTM

There are seven main steps to predicting stock prices with LSTM:

1. Data collection and preprocessing

2. Feature scaling

3. Creating training and test sets

4. Building the LSTM model

5. Training the model

6. Making predictions

7. Visualizing the results

Data Collection and Preprocessing

We’ll use historical stock price data for this tutorial. You can download stock price data from sources like Yahoo Finance. ...