The RollingValidation object provides rolling-validation, where a full dataset is divided into a training set followed by a testing set. Retraining is done as the algorithm rolls through the testing set making out-of-sample predictions/forecasts to keep the parameters from becoming stale. For example, with TR_RATIO = 0.5 and m = 1000 it works as follows: tr(ain) 0 to 499, te(st) 500 to 999 Re-training occurs according to the retraining cycle rc, e.g., rc = 10 implies that retraining would occurs after every 10 forecasts or 50 times for this example.
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TESTING SET (tr) and a TRAINING SET (te) as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls predict for one-step ahead out-of-sample forecasts.
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TESTING SET (tr) and a TRAINING SET (te) as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls predict for one-step ahead out-of-sample forecasts.
Value parameters
mod
the forecasting model being used (e.g., ARIMA)
rc
the retraining cycle (number of forecasts until retraining occurs)
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TESTING SET (tr) and a TRAINING SET (te). as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls forecast for h-steps ahead out-of-sample forecasts.
Use rolling-validation to compute test Quality of Fit (QoF) measures by dividing the dataset into a TESTING SET (tr) and a TRAINING SET (te). as follows: [ <-- tr_size --> | <-- te_size --> ] This version calls forecast for h-steps ahead out-of-sample forecasts.
Value parameters
h
the forecasting horizon (h-steps ahead)
mod
the forecasting model being used (e.g., ARIMA)
rc
the retraining cycle (number of forecasts until retraining occurs)
Test assessment and validation for the given forecasting model: (1) in-sample assessment on full dataset (2) out-of-sample validation using rolling validation with predict (one-step) (3) out-of-sample validation using rolling validation with forecast (h-steps)
Test assessment and validation for the given forecasting model: (1) in-sample assessment on full dataset (2) out-of-sample validation using rolling validation with predict (one-step) (3) out-of-sample validation using rolling validation with forecast (h-steps)
Value parameters
h
the forecasting horizon (h-steps ahead)
mod
the forecasting model to test (e.g., ARIMA)
rc
the retraining cycle (number of forecasting until retraining occurs)