Stock Price Prediction & Forecasting with LSTM Neural Networks in Python

Summary

In this educational video, Greg Hogg demonstrates forecasting Microsoft's stock price using LSTM neural networks, a valuable skill for any data science resume. The tutorial covers data acquisition from Yahoo Finance, preprocessing in pandas, and transforming it into a supervised learning problem. It then delves into model creation with TensorFlow, training, and evaluating the LSTM model's performance. The script also addresses the model's limitations in extrapolation and showcases how to make recursive predictions, offering insights into both the technical process and the practical challenges of stock prediction.

Takeaways

• 📈 The video demonstrates how to forecast Microsoft's stock using LSTM neural networks, a valuable skill for a resume.
• 🔍 The dataset for Microsoft's stock is obtained from Yahoo Finance, with the time period set to 'max' to capture the most extensive data range.
• 📊 The initial dataset includes various stock metrics, but the tutorial focuses on using the closing value for simplicity.
• 🗓 The date column in the dataset is converted from strings to actual date objects for proper data handling in the analysis.
• 👷‍♂️ The script involves creating a function to transform the data into a supervised learning problem, framing it as a time series prediction task.
• 📉 The model's input consists of the stock's closing values from the three previous days, and the output is the prediction for the next day's closing value.
• 🤖 An LSTM model is built using TensorFlow, with layers including an input layer, LSTM layer, two dense layers, and an output layer.
• 📝 The model is compiled with mean squared error as the loss function and the Adam optimizer, using mean absolute error as a metric.
• 🔧 The data is split into training, validation, and testing sets, with the model being trained and evaluated on its performance in predicting stock values.
• 📉 The model's performance is visualized using matplotlib, showing how well it predicts the stock's closing values for training, validation, and testing sets.
• 🔮 The video also explores the concept of recursive predictions, where the model uses its own predictions to forecast future stock values, highlighting the challenges in long-term forecasting.

Q & A

What is the main topic of Greg Hogg's video?

The main topic of Greg Hogg's video is forecasting Microsoft's stock using LSTM neural networks.

Why is this project considered important for a resume?

This project is considered important for a resume because it demonstrates the application of advanced machine learning techniques, specifically LSTM neural networks, to a practical and complex problem like stock prediction.

Where can the dataset for Microsoft's stock be obtained from?

The dataset for Microsoft's stock can be obtained from Yahoo Finance by navigating to the Microsoft Corporation stock page and downloading the CSV file.

What is the significance of adjusting the time period to 'max' on Yahoo Finance?

Adjusting the time period to 'max' on Yahoo Finance ensures that you download the complete historical data, providing a comprehensive dataset for analysis.

What columns of the dataset does Greg decide to use for the LSTM model?

Greg decides to use only the 'date' and 'closing value' columns for the LSTM model, as they provide the necessary information for stock price prediction.

Why is the 'date' column in the dataset initially problematic?

The 'date' column is initially problematic because it is in string format rather than a date object, which makes it unsuitable for direct use in data manipulation and analysis.

What does Greg do to convert the 'date' column into a proper date format?

Greg creates a function called 'string_to_datetime' to convert the 'date' column from string format to datetime objects, which can then be used as the index of the DataFrame.

What is the purpose of creating a 'windowed_df' DataFrame?

The 'windowed_df' DataFrame is created to convert the problem into a supervised learning problem by structuring the data with past stock closing values as inputs and the target date's closing value as the output.

How does Greg handle the issue of the model's inability to extrapolate well?

Greg addresses the issue of the model's inability to extrapolate well by training the model on more recent data, which is more representative of the current stock price trend, and limiting the prediction to the range of data seen during training.

What does Greg suggest about the practicality of using LSTM models for stock prediction on a micro scale?

Greg suggests that while LSTM models can be useful for understanding trends and making long-term investment decisions, they may not be as effective for predicting stock prices on a micro scale, such as daily buy or sell decisions.

How does Greg visualize the performance of the LSTM model on the training, validation, and test sets?

Greg visualizes the performance of the LSTM model by plotting the actual observed values against the predicted values for the training, validation, and test sets, allowing for a clear comparison of how well the model is performing across different data partitions.

What is the approach Greg takes to make recursive predictions using the trained LSTM model?

Greg uses a recursive approach where the model makes a prediction for the next day based on the last known three days of data. This predicted value is then appended to the input data, and the process is repeated to make further predictions for subsequent days.

What is the potential issue with using the model for long-term predictions as demonstrated in the script?

The potential issue with using the model for long-term predictions is that it may not adapt well to significant changes in stock price trends over time, as it tends to predict values based on the patterns observed in the training data.