# Predict Visitation Model Methods for generating predictions from objects of the class "visitation_model". ```r ## 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 ```r 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) ```

predict.visitation_model function