R/ts.seasonal.model.R

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

#' @title 
#' Model fit of seasonal time series
#'
#' @description
#' Model fit of seasonal time series
#'
#' @usage ts.seasonal.model(tsdata, x.ord = NULL, tojson = FALSE)
#' 
#' @param tsdata The input univariate seasonal time series data
#' @param x.ord An integer vector of length 3 specifying the order of the Arima model
#' @param tojson If TRUE the results are returned in json format, default returns a list
#' 
#' @details 
#' Model fit of seasonal time series using arima models of seasonal time series data.
#' The model with the lowest AIC value is selected for forecasts.
#' 
#' @return 
#' A list with the following components:
#' \itemize{
#' \item model.summary: 
#' \itemize{
#' \item ts_model The summary model details returned as Arima object for internal use in ts.analysis function}
#'
#' \item model:
#' \itemize{
#'  \item ts_model 
#'  \item arima.order The Arima order
#'  \item arima.coef A vector of AR, MA and regression coefficients
#'  \item arima.coef.se The standard error of the coefficients }
#' 
#' \item residuals_fitted:
#' \itemize{
#' \item residuals The residuals of the model (fitted innovations)
#' \item fitted The model's fitted values 
#' \item time the time of tsdata
#' \item line The y=0 line}
#'  
#' \item compare:
#' \itemize{
#'  \item variance.coef The matrix of the estimated variance of the coefficients
#'  \item resid.variance The MLE of the innovations variance
#'  \item not.used.obs The number of not used observations for the fitting
#'  \item used.obs the number of used observations for the fitting
#'  \item loglik The maximized log-likelihood (of the differenced data), or the approximation to it used
#'  \item aic The AIC value corresponding to the log-likelihood
#'  \item bic The BIC value corresponding to the log-likelihood
#'  \item aicc The second-order Akaike Information Criterion corresponding to the log-likelihood}}
#'  
#' @author Kleanthis Koupidis
#' 
#' 
#' @seealso \code{\link{ts.analysis}}, \code{\link[forecast]{Arima}}
#' 
#' @rdname ts.seasonal.model
#' @export
#' 

ts.seasonal.model <- function(tsdata, x.ord = NULL, tojson = FALSE) {
  
  if (is.null(x.ord) |
      all(x.ord == c(0, 0, 0))) {
    ts_model <- forecast::auto.arima(tsdata)
  } else {
    ts_model <- forecast::Arima(tsdata,order = x.ord)
  }
  
  model.summary <- ts_model
  model <- list(	# Model
    arima.order = ts_model$arma,
    arima.coef = ts_model$coef,
    arima.coef.se = round(sqrt(diag(ts_model$var.coef)), digits = 4))
  
  residuals_fitted <- list(
    residuals = ts_model$residuals,
    fitted = stats::fitted(ts_model),
    time = stats::time(tsdata),
    line = 0)
  
  compare <- list(
    resid.variance = ts_model$sigma2,
    variance.coef = ts_model$var.coef,
    not.used.obs = ts_model$n.cond,
    used.obs = ts_model$nobs,
    loglik = ts_model$loglik,
    aic = ts_model$aic,
    bic = ts_model$bic,
    aicc = ts_model$aicc)
  
  model.details <- list(
    model.summary = model.summary,
    model = model,
    residuals_fitted = residuals_fitted,
    compare = compare)
  
  if (tojson == TRUE) {
    model.details <- jsonlite::toJSON(model.details)
  }
  
  return(model.details)
}