Direct Forecasting
Use the directforecaster function to train a
DirectForecaster model with regularly sampled time series
data. The Statistics and Machine Learning Toolbox™ function creates a multistep forecasting model that uses a direct
strategy, where a separate regression model is trained for each step of the
forecasting horizon.
After creating a DirectForecaster model object, you can see
how the model performs on observed test data by using the loss and predict object functions. You can then use the model to forecast
at time steps beyond the available data by using the forecast object function.
Functions
compact | Reduce size of direct forecasting model (Since R2023b) |
crossval | Cross-validate direct forecasting model (Since R2023b) |
loss | Loss at each horizon step (Since R2023b) |
predict | Predict response at time steps in observed test data (Since R2023b) |
forecast | Forecast response at time steps beyond available data (Since R2023b) |
preparedPredictors | Obtain prepared data used for training or testing in direct forecasting (Since R2023b) |
cvloss | Loss for partitioned data at each horizon step (Since R2023b) |
cvpredict | Predict response using cross-validated direct forecasting model (Since R2023b) |
tspartition | Partition time series data for cross-validation (Since R2022b) |
Objects
DirectForecaster | Fit direct forecasting model (Since R2023b) |
CompactDirectForecaster | Compact direct forecasting model (Since R2023b) |
PartitionedDirectForecaster | Cross-validated direct forecasting model (Since R2023b) |
Topics
- Perform Time Series Direct Forecasting with directforecaster
Perform time series direct forecasting with the
directforecasterfunction. - Manually Perform Time Series Forecasting Using Ensembles of Boosted Regression Trees
Manually perform single-step and multiple-step time series forecasting with ensembles of boosted regression trees.
