Time Series Forecasting with Machine Learning Methods
Combine multiple horizon-specific forecast models to produce one forec...
Create model training and forecasting datasets with lagged, grouped, d...
Remove the features from a lagged training dataset to reduce memory co...
Create time-contiguous validation datasets for model evaluation
Prepare a dataset for modeling by filling in temporal gaps in data col...
Plot forecast error
Plot hyperparameters
Plot an object of class forecast_results
Plot an object of class 'forecastML'
Plot datasets with lagged features
Plot an object of class training_results
Plot validation dataset forecast error
Plot validation datasets
Predict on validation datasets or forecast
Compute forecast error
Return model hyperparameters across validation datasets
Return a summary of a lagged_df object
Train a model across horizons and validation datasets
The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.