In addition to what Richard Hardy said, we can use unobserved component model (UCM). It allows us to include regressors.

I have also seen people using Random Forest for forecasting considering other regressors. This is somewhat similar to linear regression. you can compare the fitted values against actual and test the model accuracy on a holdout sample as well to see which model is performing better.

In addition to what Richard Hardy said, we can use unobserved component model (UCM). It allows us to include regressors.

I have also seen people using Random Forest and neural network for forecasting considering other regressors. You can compare the fitted values against actual and test the model accuracy on a holdout sample as well to see which model is performing better.