VisitorCounts (version 2.0.3)

predict.visitation_model: Predict Visitation Model

Description

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

Usage

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

Value

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.

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.

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)