Description Usage Arguments Details Value Examples
forecast_var_lasso
forecasts time series using VAR-lasso model
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y |
multivariate time series |
h |
forecast horizon. Will be used for cross-validation in honest type. |
model |
estimated VAR-lasso model. If missing will be estimated automatically. |
p |
number of lags |
scale |
logical, If TRUE then time series are scaled to mean = 0 and sd = 1 before estimation. Forecasts are scaled back to original mean and sd. |
struct |
type of VAR-lasso |
gran |
granularity vector, If gran = (a, b) then cross-validation checks b values form lambda to lambda/a. |
type |
fast or honest cross-validation. honest: cross-validation is repeated for each h, so h should be a vector. model should be missing or a list of the same length as h or the same model for all h. fast: do cross-validation for h_cv and than predict for each horizon from 1 to h. |
h_cv |
forecast horizon used for cross validation. Will be used for cross-validation in fast type. |
... |
further arguments passed to |
cv.BigVAR
function from BigVAR
package is used
forecasts from VAR-lasso model as mforecast object
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