November 2018
Intermediate to advanced
322 pages
7h 54m
English
Content preview from TensorFlow Machine Learning Projects
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6: Predicting Stock Prices using Gaussian Process Regression
In this chapter, we will learn about a new model for forecasting known as Gaussian processes, popularly abbreviated as GPs, this is extremely popular in forecasting applications where we want to model non-linear functions with a few data points and also to quantify uncertainty in predictions.
We will use Gaussian processes to predict the stock prices of three major stocks, namely, Google, Netflix, and the General Electric (GE) company.
The rest of this chapter is divided into the following sections:
- Understanding Bayes' rule
- Bayesian inference
- Introducing Gaussian processes
- Understanding the stock market dataset
- Applying Gaussian processes to predict stock market prices
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