# Aggregation of Interval Predictions

Given a montly time series, my objective is to provide my client with the next 12 point forecasts along with a yearly forecast. To obtain the yearly forecast, I simply sum up the 12 points forecasts. However, I don't know how to estimate the prediction interval associated with this sum.

Any ideas on how to obtain a prediction interval for the yearly forecast based on the prediction intervals estimated for each month?

Thanks

• Got to to but here is the R about it: library(forecast); ets_fit=ets(USAccDeaths, model="ZZZ"); horizon=12; deaths.fcast <- forecast(ets_fit,h=horizon); n_sim=5000 set.seed(123) sims = replicate(n_sim, sum(simulate(ets_fit, future=TRUE, nsim=horizon)) ); 100*(mean(sims) - sum(deaths.fcast$mean)) / sum(deaths.fcast$mean) Effectively: when we sum up the 12 point forecasts, we sum up a trajectory of length 12. That is because our prediction $\hat{y}_{t^*=t+5}$ is contingent to that of $\hat{y}_{t^*=t+4}$, etc. we therefore have to simulate these trajectories and then aggregated them. Mar 23, 2022 at 13:43
• Mar 24, 2022 at 2:37
• Thank you @usεr11852 and Rob Hyndman . The equivalent code in Python exist ? Mar 24, 2022 at 9:54
• By the linearity of expectation the sum of the expected value of each forecast should be equal to the expected value of the sum. However the variance of the sum is not the sum of the variances due to the dependence between observations. One thing you could do is fit a model which predicts the next 12 months of observations and use this to calculate a prediction interval. Jun 1, 2022 at 16:45

When creating the yearly forecasts by summing "up the 12 points forecasts", we actually sum the predicted trajectory across those 12 forward steps. It is important to emphasise this because it highlights that we do not have independent predictions. (i.e. our predictions for $$\hat{y}_{t=5}$$ is contingent to our $$\hat{y}_{t=4}$$ prediction, etc.) With that in mind, we want to create multiply trajectories (e.g. via bootstrapping) and then sum them up to get our final interval predictions for our yearly aggregates.

A quick R example can be found here:

library(forecast);
ets_fit = ets(USAccDeaths, model="ZZZ");
horizon = 12;
deaths.fcast = forecast(ets_fit, h=horizon);
n_sim=5000;
set.seed(123);
sims = replicate(n_sim, sum(simulate(ets_fit, future=TRUE, nsim=horizon)));
# Proportional difference between predicted mean and simulated mean
100*(mean(sims) - sum(deaths.fcast$$mean)) / sum(deaths.fcast$$mean)


Python has a number of time-series libraries that are all great but none of them has emerged as the dominant one as forecast in R has. I have used PyFlux in the past. It has the ability to create posterior predictive checks for the mean prediction (e.g. look at plot_ppc() member methods which are available for most models) allowing you to specify nsims, etc. sktime also seems to have the ability to simulate a new time series following a state-space model but I don't see any worked examples in its documentation, seems possible though.