Forecasting with exogenous variables and ensemble learning

This recipe will allow you to explore two different techniques: working with multivariate time series and using ensemble forecasters. For example, the EnsembleForecaster class takes in a list of multiple regressors, each regressor gets trained, and collectively contribute in making a prediction. This is accomplished by taking the average of the individual predictions from each regressor. Think of this as the power of the collective. You will use the same regressors you used earlier: Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, and Support Vector Machines Regressor.

You will use a Naive Regressor as the baseline to compare with EnsembleForecaster. Additionally, you will use exogenous variables with the Ensemble Forecaster to model a multivariate time series. You can use any regressor that accepts exogenous variables.

Getting ready

You will load the same modules and libraries from the previous...