Using Gaussian Processes for Financial Time Series Forecasting
In the field of finance, accurate forecasting of stock prices and other financial time series data is of utmost importance. Traders and investors rely on these forecasts to make informed decisions and maximize their profits. Traditional forecasting methods, such as ARIMA and GARCH models, have been widely used, but they often fail to capture the complex patterns and non-linear relationships present in financial data.
In recent years, Gaussian Processes (GPs) have gained popularity as a powerful tool for time series forecasting. GPs are a flexible and non-parametric approach that can capture complex patterns and uncertainties in the data. In this tutorial, we will explore how to use Gaussian Processes for financial time series forecasting in Python.
Prerequisites
To follow along with this tutorial, you should have a basic understanding of Python programming and familiarity with financial time series data. You will also need to install the following Python libraries:
pip install numpy
pip install pandas
pip install matplotlib
pip install yfinance
pip install scikit-learn
pip install GPy