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Using LSTM to Predict Stock Volatility Using Data From FMP
Creating a Volatility Auto-Regressive Forecast Model Using LSTM
Neural networks, including their specialized forms like LSTMs, have revolutionized machine learning and artificial intelligence. By mimicking the brain’s structure and learning from data, they enable machines to perform tasks that were once thought to be exclusively human domains.
Understanding the basics of neural networks and the unique advantages of LSTMs provides a solid foundation for exploring more advanced topics and applications in the field.
This article will introduce these complex structures and show how to predict the volatility of Apple’s stock price using data imported from FMP.
A Primer in Neural Networks and LSTMs
Neural networks are a class of machine learning algorithms inspired by the human brain’s structure and function. They consist of interconnected nodes or neurons that work together to solve complex problems. Neural networks are particularly well-suited for tasks involving pattern recognition, classification, regression, and generation of new data. The basic structure of a neural network is composed of mainly three elements:
- Input layer: The first layer, where the…
