It is common to want forecasts to be positive, or to require them to be within some specified range $[a,b]$. Both of these situations are relatively easy to handle using transformations.

### Positive forecasts

To impose a positivity constraint, simply work on the log scale. With the forecast package in R, this can be handled by specifying the Box-Cox parameter $\lambda=0$. For example, consider the real price of a dozen eggs (1900-1993; in cents):

library(fpp) fit <- ets(eggs, lambda=0) plot(forecast(fit, h=50)) |

### Forecasts constrained to an interval

To see how to handle data constrained to an interval, imagine that the egg prices were constrained to lie within $a=50$ and $b=400$. Then we can transform the data using a scaled logit transform which maps $(a,b)$ to the whole real line:

$$

y = \log\left(\frac{x-a}{b-x}\right)

$$

where $x$ is on the original scale and $y$ is the transformed data.

# Bounds a <- 50 b <- 400 # Transform data y <- log((eggs-a)/(b-eggs)) fit <- ets(y) fc <- forecast(fit, h=50) # 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) |

The prediction intervals from these transformations have the same coverage probability as on the transformed scale, because quantiles are preserved under monotonically increasing transformations.

### Related Posts:

- The forecast mean after back-transformation
- Transforming data with zeros
- Major changes to the forecast package
- New in forecast 5.0
- Errors on percentage errors