data("salmAllOnset")
## fit a hhh4 model to the first 13 years
salmModel <- list(end = list(f = addSeason2formula(~1 + t)),
ar = list(f = ~1), family = "NegBin1", subset = 2:678)
salmFit <- hhh4(salmAllOnset, salmModel)
## simulate the next 20 weeks ahead (with very small 'nsim' for speed)
salmSims <- simulate(salmFit, nsim = 500, seed = 3, subset = 678 + seq_len(20),
y.start = observed(salmAllOnset)[678,])
if (requireNamespace("fanplot"))
plot(salmSims, "fan")
### calculate scores at each time point
## using empirical distribution of simulated counts as forecast distribution
scores(salmSims, which = c("rps", "logs", "dss"))
## observed count sometimes not covered by simulations -> infinite log-score
## => for a more detailed forecast, either considerably increase 'nsim', or:
## 1. use continuous density() of simulated counts as forecast distribution
fi <- apply(salmSims, 1, function (x) approxfun(density(x)))
logs_kde <- mapply(function (f, y) -log(f(y)),
f = fi, y = observed(attr(salmSims,"stsObserved")))
cbind("empirical" = scores(salmSims, "logs"), "density" = logs_kde)
## a similar KDE approach is implemented in scoringRules::logs_sample()
## 2. average conditional predictive NegBin's of simulated trajectories,
## currently only implemented in HIDDA.forecasting::dhhh4sims()
### produce a PIT histogram
## using empirical distribution of simulated counts as forecast distribition
pit(x = observed(attr(salmSims, "stsObserved")),
pdistr = apply(salmSims, 1:2, ecdf))
## long-term forecast is badly calibrated (lower tail is unused, see fan above)
## we also get a warning for the same reason as infinite log-scores
Run the code above in your browser using DataLab