R/ts.forecast.R

In TimeSeries.OBeu: Time Series Analysis 'OpenBudgets.eu'

#' @title 
#' Time series forecast results of OBEU Time Series
#'  
#' @description
#' Univariate time series forecasts for short and long time series data using the appropriate model.
#' 
#' @usage ts.forecast(ts_modelx, h = 1, tojson = FALSE)
#' 
#' @param ts_modelx The input univariate time series data
#' @param h The number of prediction steps
#' @param tojson If TRUE the results are returned in json format, default returns a list
#' 
#' @details 
#' This function is used internally in ts.analysis and forecasts the model 
#' that fits the input data using the auto.arima function(see forecast package). 
#' The model selection depends on the results of some diagnostic tests (acf,pacf,pp adf and kpss).
#' For short time series the selected arima model is among various orders of the AR part using 
#' the first differences and the first order moving average component, with the lower AIC value.
#' 
#' @return A list with the parameters:
#' 
#' \itemize{ 
#'
#' \item ts.model a string indicating the arima orders
#' 
#' \item data_year The time that time series data were sampled
#'
#' \item data The time series values
#' \item predict_time The time that defined by the prediction_steps parameter
#' \item predict_values The predicted values that defined by the prediction_steps parameter
#' \item up80: The upper limit of the 80\% predicted confidence interval
#' \item low80: The lower limit of the 80\% predicted confidence interval
#' \item up95: The upper limit of the 95\% predicted confidence interval
#' \item low95: The lower limit of the 95\% predicted confidence interval}
#'
#' @author Kleanthis Koupidis, Charalampos Bratsas
#' 
#' 
#' @seealso \code{\link{ts.analysis}}, \code{\link[forecast]{forecast}}
#' 
#' @examples
#' Athens_draft <- ts.non.seas.model(Athens_draft_ts)
#' #Hold the model object of non seasonal modeling
#' draft <- Athens_draft$model.summary 
#' ts.forecast(draft)
#' 
#' @rdname ts.forecast
#'
#' @export
#' 

ts.forecast <- function(ts_modelx, h = 1, tojson = FALSE) {
  
  ## Model Forecasting
  forecasts <- forecast::forecast(ts_modelx, h)
  
  forecast.param <- list(
    ts.model = forecasts$method,
    data_year = stats::time(ts_modelx$x),
    data = ts_modelx$x,
    predict_time = stats::time(forecasts$mean),
    predict_values = forecasts$mean,
    up80 = forecasts$upper[,"80%"],
    low80 = forecasts$lower[,"80%"],
    up95 = forecasts$upper[,"95%"],
    low95 = forecasts$lower[,"95%"])
  
  if (tojson == TRUE) {
    forecast.param=jsonlite::toJSON(forecast.param)
  }
  
  ##  Result
  return(forecast.param)
}