I've been working with bsts to model time-series and make forecasts, but have run into some problems with the predict function and holidays.
When I train the model with regressor holidays these regressors do not appear to be used in the predict function. For instance in a simple local level model the holiday regressor causes the response variable to go down, but the predictor of the response over the same period does not capture this behaviour.
I attach below a minimum working example based on the regression.holiday code from the documentation:
library(bsts)
library(ggplot2)
set.seed(12345)
trend <- cumsum(rnorm(1095, 0, .1))
dates <- seq.Date(from = as.Date("2014-01-01"), length = length(trend), by = "day")
y <- zoo(trend + rnorm(length(trend), 0, .2), dates)
AddHolidayEffect <- function(y, dates, effect) {
## Adds a holiday effect to simulated data.
## Args:
## y: A zoo time series, with Dates for indices.
## dates: The dates of the holidays.
## effect: A vector of holiday effects of odd length. The central effect is
## the main holiday, with a symmetric influence window on either side.
## Returns:
## y, with the holiday effects added.
time <- dates - (length(effect) - 1) / 2
for (i in 1:length(effect)) {
y[time] <- y[time] + effect[i]
time <- time + 1
}
return(y)
}
## Define some holidays.
memorial.day <- NamedHoliday("MemorialDay")
memorial.day.effect <- c(-.75, -2, -2)
memorial.day.dates <- as.Date(c("2014-05-26", "2015-05-25", "2016-05-25"))
y <- AddHolidayEffect(y, memorial.day.dates, memorial.day.effect)
## The holidays can be in any order.
holiday.list <- list(memorial.day)
## Let's train the model to just before MemorialDay
cut_date = as.Date("2016-05-20")
train_data <- y[time(y) < cut_date]
test_data <- y[time(y) >= cut_date]
ss <- AddLocalLevel(list(), train_data)
ss <- AddRegressionHoliday(ss, train_data, holiday.list = holiday.list)
model <- bsts(train_data, state.specification = ss, niter = 500, ping = 0)
## Now let's make a prediction covering MemorialDay
my_horizon = 15
## Note adding the time stamps here doesn't help either
pred <- predict(object = model, horizon = my_horizon)
## Make a data frame for plotting
plot_info <- data.frame(Date = time(y),
value = y,
predict_mean = NA,
predict_upper = NA,
predict_lower = NA
)
plot_info[plot_info$Date %in% time(test_data)[1:my_horizon],]$predict_mean = pred$mean
plot_info[plot_info$Date %in% time(test_data)[1:my_horizon],]$predict_lower = pred$interval[1,]
plot_info[plot_info$Date %in% time(test_data)[1:my_horizon],]$predict_upper = pred$interval[2,]
## Let's make a pretty plot to demonstrate the problem
filter(plot_info, Date > time(test_data)[1] - 25 & Date < time(test_data)[my_horizon] + 10) %>%
ggplot(aes(x = Date, y = value)) +
geom_line() +
geom_line(aes(y = predict_mean), col = "Forest Green") + # The prediction
geom_line(aes(y = predict_lower), col = "Forest Green", lty = 2) + # lower bound
geom_line(aes(y = predict_upper), col = "Forest Green", lty = 2) # upper bound
Hi!
I've been working with bsts to model time-series and make forecasts, but have run into some problems with the predict function and holidays.
When I train the model with regressor holidays these regressors do not appear to be used in the predict function. For instance in a simple local level model the holiday regressor causes the response variable to go down, but the predictor of the response over the same period does not capture this behaviour.
I attach below a minimum working example based on the regression.holiday code from the documentation: