I was recently asked how to implement time series cross-validation in R. Time series people would normally call this “forecast evaluation with a rolling origin” or something similar, but it is the natural and obvious analogue to leave-one-out cross-validation for cross-sectional data, so I prefer to call it “time series cross-validation”.

Here is some example code applying time series CV and comparing 1-step, 2-step, …, 12-step forecasts using the Mean Absolute Error (MAE). Here I compare (1) a linear model containing trend and seasonal dummies applied to the log data; (2) an ARIMA model applied to the log data; and (3) an ETS model applied to the original data. The code is slow because I am estimating an ARIMA and ETS model for each iteration. (I’m also estimating a linear model, but that doesn’t take long.)

library(fpp) # To load the data set a10 plot(a10, ylab="$ million", xlab="Year", main="Antidiabetic drug sales") plot(log(a10), ylab="", xlab="Year", main="Log Antidiabetic drug sales") k <- 60 # minimum data length for fitting a model n <- length(a10) mae1 <- mae2 <- mae3 <- matrix(NA,n-k,12) st <- tsp(a10)[1]+(k-2)/12 for(i in 1:(n-k)) { xshort <- window(a10, end=st + i/12) xnext <- window(a10, start=st + (i+1)/12, end=st + (i+12)/12) fit1 <- tslm(xshort ~ trend + season, lambda=0) fcast1 <- forecast(fit1, h=12) fit2 <- Arima(xshort, order=c(3,0,1), seasonal=list(order=c(0,1,1), period=12), include.drift=TRUE, lambda=0, method="ML") fcast2 <- forecast(fit2, h=12) fit3 <- ets(xshort,model="MMM",damped=TRUE) fcast3 <- forecast(fit3, h=12) mae1[i,1:length(xnext)] <- abs(fcast1[['mean']]-xnext) mae2[i,1:length(xnext)] <- abs(fcast2[['mean']]-xnext) mae3[i,1:length(xnext)] <- abs(fcast3[['mean']]-xnext) } plot(1:12, colMeans(mae1,na.rm=TRUE), type="l", col=2, xlab="horizon", ylab="MAE", ylim=c(0.65,1.05)) lines(1:12, colMeans(mae2,na.rm=TRUE), type="l",col=3) lines(1:12, colMeans(mae3,na.rm=TRUE), type="l",col=4) legend("topleft",legend=c("LM","ARIMA","ETS"),col=2:4,lty=1) |

This yields the following figure.

A useful variation on this procedure is to keep the training window of fixed length. In that case, replace the first line in the for loop with

xshort <- window(a10, start=st+(i-k+1)/12, end=st+i/12) |

Then the training set always consists of k observations.

Another variation is to compute one-step forecasts in the test set. Then the body of the for loop should be replaced with the following.

xshort <- window(a10, end=st + i/12) xnext <- window(a10, start=st + (i+1)/12, end=st + (i+12)/12) xlong <- window(a10, end=st + (i+12)/12) fit1 <- tslm(xshort ~ trend + season, lambda=0) fcast1 <- forecast(fit1, h=12)$mean fit2 <- Arima(xshort, order=c(3,0,1), seasonal=list(order=c(0,1,1), period=12), include.drift=TRUE, lambda=0, method="ML") fit2a <- Arima(xlong, model=fit2, lambda=0) fcast2 <- fitted(fit2a)[-(1:length(xshort))] fit3 <- ets(xshort,model="MMM",damped=TRUE) fit3a <- ets(xlong, model=fit3) fcast3 <- fitted(fit3a)[-(1:length(xshort))] mae1[i,1:length(xnext)] <- abs(fcast1-xnext) mae2[i,1:length(xnext)] <- abs(fcast2-xnext) mae3[i,1:length(xnext)] <- abs(fcast3-xnext) |

Here the models are fitted to the training set (`xshort`

), and then applied to the longer data set (`xlong`

) without re-estimating the parameters. So the fitted values from the latter are one-step forecasts on the whole data set. Therefore, the last part of the fitted values vector are one-step forecasts on the test set.

Yet another variation which is useful for large data sets is to use a form of k-fold cross-validation where the training sets increment by several values at a time. For example, instead of incrementing by one observation in each iteration, we could shift the training set forward by 12 observations.

k <- 60 # minimum data length for fitting a model n <- length(a10) mae1 <- mae2 <- mae3 <- matrix(NA,12,12) st <- tsp(a10)[1]+(k-1)/12 for(i in 1:12) { xshort <- window(a10, end=st + (i-1)) xnext <- window(a10, start=st + (i-1) + 1/12, end=st + i) fit1 <- tslm(xshort ~ trend + season, lambda=0) fcast1 <- forecast(fit1, h=12) fit2 <- Arima(xshort, order=c(3,0,1), seasonal=list(order=c(0,1,1), period=12), include.drift=TRUE, lambda=0, method="ML") fcast2 <- forecast(fit2, h=12) fit3 <- ets(xshort,model="MMM",damped=TRUE) fcast3 <- forecast(fit3, h=12) mae1[i,] <- abs(fcast1[['mean']]-xnext) mae2[i,] <- abs(fcast2[['mean']]-xnext) mae3[i,] <- abs(fcast3[['mean']]-xnext) } plot(1:12, colMeans(mae1), type="l", col=2, xlab="horizon", ylab="MAE", ylim=c(0.35,1.5)) lines(1:12, colMeans(mae2), type="l",col=3) lines(1:12, colMeans(mae3), type="l",col=4) legend("topleft",legend=c("LM","ARIMA","ETS"),col=2:4,lty=1) |

However, because this is based on fewer estimation steps, the results are much more volatile. It may be best to average over the forecast horizon as well:

> mean(mae1) [1] 0.8053712 > mean(mae2) [1] 0.7118831 > mean(mae3) [1] 0.792813 |

The above code assumes you are using v3.02 or later of the forecast package.

### Related Posts:

- Fitting models to short time series
- New in forecast 6.0
- Cyclic and seasonal time series
- Measuring time series characteristics
- Fast computation of cross-validation in linear models

Pingback: Time Series Cross Validation | Jeffrey Chin Tang Wong()

Pingback: Hyndsight - ARIMA models with long lags()

Pingback: Distilled News | Data Analytics & R()