Introduction to Gaussian Processes

In this recipe, we’ll introduce Gaussian Processes (GP), a powerful algorithm for probabilistic machine learning.

Getting ready

GP offers a flexible, probabilistic approach to modeling in machine learning. This section introduces the concept of GP and prepares the necessary environment for forecasting using a GP model.

We need to import a new library to be able to fit GP, namely gpytorch:

import torch
import gpytorch
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from pytorch_lightning import LightningDataModule

Then, we must read the multivariate time series data and process it, scaling both the features and target variable, as scaled data typically improves GP modeling performance significantly.

How to do it…

We’ll use the gpytorch library to implement a GP model:

1. The key components in a GP model are the...