Univariate models only use the date and target variable values when producing a forecast. They are mostly common on various statistical forecasting models like arima and ets.
Multivariate models leverage many features when producing a forecast, provided as input data before model training. These features can be automatically created using internal feature engineering techniques within the package, or provided as external regressors. Most common machine learning models today, like xgboost and cubist, are multivariate models. An important thing to note is that multivariate models provided in the package can leverage different recipes of feature engineering, that contain different techniques of creating features. These can be identified by seeing the letter “R” followed by a number like “1” or “2”. More info can be found in the feature engineering vignette.
Global models take the entire data set across all individual time series and model them all at once within a single model. Global models are only ran if the input data contains more than one individual time series.
Local models take each individual time series from the input data and model them separately.
Ensemble models are trained on predictions made by individual models. For example, a glmnet ensemble model takes forecasts made by each individual model and feeds them as training data into a glmnet model.
Most of the models within the package are built on a fantastic time series library called modeltime, which was built on top tidymodels. Tidymodels is a fantastic series of packages that help in feature engineering (recipes), hyperparameter tuning (tune), model training (parsnip), and back testing (resample). Big shout out to the modeltime and tidymodels teams for being the shoulders this package stands on!