Simulating from TBATS models

Date

18 February 2022

Topics

time series

forecasting

data science

I’ve had several requests for an R function to simulate future values from a TBATS model. We will eventually include TBATS in the fable package, and the facilities will be added there. But in the meantime, if you are using the forecast package and want to simulate from a fitted TBATS model, here is how do it.

Simulating via one-step forecasts

Doing it efficiently would require a more complicated approach, but this is super easy if you are willing to sacrifice some speed. The trick is to realise that a simulation can be handled easily for almost any time series model using residuals and one-step forecasts. Note that a residual is given by e_t = y_t - \hat{y}_{t|t-1} so we can write

y_t = \hat{y}_{t|t-1} + e_t.

Therefore, given data to time T, we can simulate iteratively using y_{T+i}^* = \hat{y}_{T+i|T+i-1} + \varepsilon_{T+i}, \qquad i=1,\dots,h, where \varepsilon_{T+i} is randomly generated from the error distribution, or bootstrapped by randomly sampling from past residuals. The value of \hat{y}_{T+i|T+i-1} can be obtained by applying the model to the series \\{y_1,\dots,y_T,y^*_{T+1},\dots,y^*_{T+i-1}\\} (without re-estimating the parameters) and forecasting one-step ahead. This is the same trick we use to get prediction intervals for neural network models.

Implementation

Because simulate() is an S3 method in R, we have to make sure the corresponding simulate.tbats() function has all the necessary arguments to match other simulate functions. It’s also good to make it as close as possible to other simulate functions in the forecast package, to make it easier for users when switching between them. The real work is done in the last few lines.

simulate.tbats <- function(object, nsim=length(object$y),
                           seed = NULL, future=TRUE,
                           bootstrap=FALSE, innov = NULL, ...) {
  if (is.null(innov)) {
    if (!exists(".Random.seed", envir = .GlobalEnv)) {
      runif(1)
    }
    if (is.null(seed)) {
      RNGstate <- .Random.seed
    }
    else {
      R.seed <- .Random.seed
      set.seed(seed)
      RNGstate <- structure(seed, kind = as.list(RNGkind()))
      on.exit(assign(".Random.seed", R.seed, envir = .GlobalEnv))
    }
  }
  else {
    nsim <- length(innov)
  }
  if (bootstrap) {
    res <- residuals(object)
    res <- na.omit(res - mean(res, na.rm = TRUE))
    e <- sample(res, nsim, replace = TRUE)
  }
  else if (is.null(innov)) {
    e <- rnorm(nsim, 0, sqrt(object$variance))
  } else {
    e <- innov
  }
  x <- getResponse(object)

  y <- numeric(nsim)
  if(future) {
    dataplusy <- x
  } else {
    # Start somewhere in the original series
    dataplusy <- ts(sample(x, 1), start=-1/frequency(x),
                    frequency = frequency(x))
  }
  fitplus <- object
  for(i in seq_along(y)) {
    y[i] <- forecast(fitplus, h=1)$mean + e[i]
    dataplusy <- ts(c(dataplusy, y[i]),
                    start=start(dataplusy), frequency=frequency(dataplusy))
    fitplus <- tbats(dataplusy, model=fitplus)
  }
  return(tail(dataplusy, nsim))
}

I’ve added this to the forecast package for the next version.

Something similar could be written for any other forecasting function that doesn’t already have a simulate method. Just swap the tbats call to the relevant modelling function.

Example

library(forecast)
library(ggplot2)

fit <- tbats(USAccDeaths)
p <- USAccDeaths %>% autoplot() +
  labs(x = "Year", y = "US Accidental Deaths",
       title = "TBATS simulations")
for (i in seq(9)) {
  p <- p + autolayer(simulate(fit, nsim = 36), series = paste("Sim", i))
}
p