Projection-based functional data model — projection

Model and forecast functional data using a Hyndman and Ullah projection-based model.

Usage

projection_model(
  X,
  TVE = 0.95,
  model = c("ets", "arima"),
  n.ahead = 0,
  alpha = 0.05,
  check.cp = TRUE,
  frequency = 1,
  ...
)

Arguments

X: A dfts object or data which can be automatically converted to that format. See dfts().
TVE: Numeric in [0,1] for the total variance explained to select number of PCA components to use to model the data.
model: String to indicate method to model components, either "ets" or "arima".
n.ahead: Number of observations to forecast.
alpha: Significance in [0,1] for intervals when forecasting.
check.cp: Boolean which indicates if the errors should be checked for change points to change forecasts and plots.
frequency: Numeric for seasonal frequency when component is made a ts object for the models.
...: Additional information to pass into pca, change (if check.cp=TRUE), and plot.

Value

List with the following elements:

fit: dfts object for fit.
forecast_plot: plot of the data with any forecasted values.
residuals: dfts object for residuals from the fit.
changes: vector of any changes when using detect.cp.
component_models: modeled PCs from the data.
component_true: true data constucted via the PCs.
parameters: list with fit parameters like pcs, TVE, model, and n.ahead.

References

Hyndman, R. J., & Shahid Ullah, M. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 51(10), 4942-4956. https://doi.org/10.1016/j.csda.2006.07.028

Examples

result <- projection_model(dfts(electricity$data[,50:100]),
 n.ahead=1, TVE=0.1, check.cp=FALSE)