The forecast mean after back-transformation

February 24, 2014
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(This article was first published on Hyndsight » R, and kindly contributed to R-bloggers)

Many functions in the forecast package for R will allow a Box-Cox transformation. The models are fitted to the transformed data and the forecasts and prediction intervals are back-transformed. This preserves the coverage of the prediction intervals, and the back-transformed point forecast can be considered the median of the forecast densities (assuming the forecast densities on the transformed scale are symmetric). For many purposes, this is acceptable, but occasionally the mean forecast is required. For example, with hierarchical forecasting the forecasts need to be aggregated, and medians do not aggregate but means do.

It is easy enough to derive the mean forecast using a Taylor series expansion. Suppose f(x) represents the back-transformation function, \mu is the mean on the transformed scale and \sigma^2 is the variance on the transformed scale. Then using the first three terms of a Taylor expansion around \mu, the mean on the original scale is given by

    \[f(\mu) + \frac{1}{2}\sigma^2f''(\mu).\]

Box-Cox transformations

For a Box-Cox transformation,

    \[f(x) = \left\{\begin{array}{ll}  (\lambda x+1)^{1/\lambda} & \text{if $\lambda\ne0$;}\\  e^x & \text{if $\lambda=0$.}\end{array}\right.\]

So

    \[f''(x) = \left\{\begin{array}{ll} (1-\lambda)(\lambda x+1)^{1/\lambda-2} & \text{if $\lambda\ne0$;}\\  e^x & \text{if $\lambda=0$.}\end{array}\right.\]

and the backtransformed mean is given by

    \[\left\{\begin{array}{ll}  (\lambda \mu+1)^{1/\lambda}\left[1 + \frac{\sigma^2(1-\lambda)}{2(\lambda \mu+1)^{2}}\right] & \text{if $\lambda\ne0$;}\\  e^\mu\left[1 + \frac{\sigma^2}{2}\right] & \text{if $\lambda=0$.}\end{array}\right.\]

Therefore, to adjust the back-transformed mean obtained by R, the following code can be used.

library(fpp)
 
fit <- ets(eggs, lambda=0)
fc <- forecast(fit, h=50, level=95)
fvar <- ((BoxCox(fc$upper,fit$lambda)-BoxCox(fc$lower,fit$lambda))/qnorm(0.975)/2)^2
plot(fc)
fc$mean <- fc$mean * (1 + 0.5*fvar)
lines(fc$mean,col='red')
 
fit <- ets(eggs, lambda=0.2)
fc <- forecast(fit, h=50, level=95)
fvar <- ((BoxCox(fc$upper,fit$lambda)-BoxCox(fc$lower,fit$lambda))/qnorm(0.975)/2)^2
plot(fc)
fc$mean <- fc$mean * (1 + 0.5*fvar*(1-fit$lambda)/(fc$mean)^(2*fit$lambda))
lines(fc$mean,col='red')

The second of these plots is shown below. The blue line shows the forecast medians while the red line shows the forecast means.

eggs3

Scaled logistic transformation

In my previous post on transformations, I described the scaled logit transformation for bounding a forecast between specified limits a and b. In this case,

    \[f^{-1}(x) = \log\left(\frac{x-a}{b-x}\right)\]

and so

    \begin{align*} f(x) &= \frac{a+be^x}{1+e^x},\\ %f'(x) &= \frac{e^x(b - a)}{(1+e^x)^2} \\ f''(x) &= \frac{(b-a)e^x(1-e^{x})}{(1+e^x)^3},  \end{align*}

and the back-transformed mean is given by

    \[\frac{1}{(1+e^\mu)^3} \left[ (a+be^\mu)(1+e^\mu)^2 + \frac{\sigma^2}{2}(b-a)e^\mu(1-e^{\mu})\right]\]

In R, this can be calculated as follows.

# Bounds
a <- 50
b <- 400
# Transform data
y <- log((eggs-a)/(b-eggs))
fit <- ets(y)
fc <- forecast(fit, h=50, level=0.95)
fvar <- ((fc$upper=fc$lower)/qnorm(0.975)/2)^2
emu <- exp(fc$mean)
# Back-transform forecasts
fc$mean <- (b-a)*exp(fc$mean)/(1+exp(fc$mean)) + a
fc$lower <- (b-a)*exp(fc$lower)/(1+exp(fc$lower)) + a
fc$upper <- (b-a)*exp(fc$upper)/(1+exp(fc$upper)) + a
fc$x <- eggs
# Plot result on original scale
plot(fc)
# Compute forecast mean
fc$mean <- 1/(1+emu)^3*((a+b*emu)*(1+emu)^2 + fvar*(b-a)*emu*(1-emu)/2)
lines(fc$mean,col='red')

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