predict.visitation_model function

Predict Visitation Model

Methods for generating predictions from objects of the class "visitation_model".

## S3 method for class 'visitation_model'
predict(
  object,
  n_ahead,
  only_new = TRUE,
  past_observations = c("fitted", "reference"),
  ...
)

Arguments

• object: An object of class "visitation_model".
• n_ahead: An integer indicating how many observations to forecast.
• only_new: A Boolean specifying whether to include only the forecasts (if TRUE) or the full reconstruction (if FALSE). The default option is TRUE.
• past_observations: A character string; one of "fitted" or "reference". Here, "fitted" uses the fitted values of the visitation model, while "reference" uses values supplied in `ref_series'.
• ...: Additional arguments.

Returns

A predictions for the automatic decomposition. - forecasts: A vector with forecast values.

• n_ahead: A numeric that shows the number of steps ahead.

• proxy_forecasts: A vector for the proxy of trend forecasts.

• onsite_usage_forecasts: A vector for the visitation forecasts.

• beta: A numeric for the seasonality adjustment factor.

• constant: A numeric for the value of the constant in the model.

• slope: A numeric for the value of the slope term in the model when trend is set to "linear".

• criterion: A string which specifies the method used to select the appropriate lag. Only applicable if the trend component is part of the forecasts.

• past_observations: A vector which specifies the fitted values for the past observations.

• lag_estimate: A numeric for the estimated lag. Only applicable if the trend component is part of the forecasts.

Examples

data("park_visitation")
data("flickr_userdays")

n_ahead <- 36
park <- "ROMO"
pud_ts <- ts(park_visitation[park_visitation$park == park,]$pud, start = 2005, frequency = 12)
pud_ts <- log(pud_ts)

nps_ts <- ts(park_visitation[park_visitation$park == park,]$nps, start = 2005, frequency = 12)
nps_ts <- log(nps_ts)
popularity_proxy <- log(flickr_userdays)

vm <- visitation_model(pud_ts,popularity_proxy, ref_series = nps_ts, trend = "linear")
predict_vm <- predict(vm,n_ahead,
                      only_new = FALSE, past_observations = "reference")
plot(predict_vm, )
predict_vm2 <- predict(vm,n_ahead,
                       only_new = FALSE, past_observations = "reference")
plot(predict_vm2)