A C++ library for Bayesian modeling, mainly through Markov chain Monte Carlo, but with a few other methods supported. BOOM = "Bayesian Object Oriented Modeling". It is also the sound your computer makes when it crashes.
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bsts hangs with MonthlyAnnualCycle and short data #46
I've found a couple of series that can be modeled with only a monthly-annual cycle component and as little as 34 days, but a series with too few days seems to get stuck in a loop. The following example hangs when trying to model 60 days.
library(bsts)
data <- zoo(runif(100, 0, 10), seq.Date(from = as.Date("2020-01-01"), by = 1, length.out = 100))
for (i in length(data):1) {
print(i)
state <- AddMonthlyAnnualCycle(state.specification = list(), y = data[1:i])
bsts(data[1:i], state.specification = state, niter = 10, ping = 0)
}
It could be as simple as a test for length of y in the AddMonthlyAnnualCycle() function, but because I've found spots where it didn't hang until there were even fewer observations, I'm unsure. I assume it might be a matter of needing to have the entirety of at least one full calendar month, with a day on either side? If so, the test would need to be for that.
After testing, it looks like it does require a full calendar month and one day before and after to that calendar month. The following runs:
library(bsts)
data <- zoo(runif(100, 0, 10), seq.Date(from = as.Date("2019-12-31"), by = 1, length.out = 33))
state <- AddMonthlyAnnualCycle(state.specification = list(), y = data)
bsts(data, state.specification = state, niter = 10, ping = 0)
Shortening the data by one observation on either end will cause it to hang. The following logical statement tests this on a tibble of data:
!tbl_data %>%
dplyr::count(month = lubridate::floor_date(index, unit = "month")) %>%
dplyr::mutate(
days_in_month = lubridate::days_in_month(month),
test = ifelse(n == days_in_month & dply::lag(n) > 0 & dply::lead(n) > 0, TRUE, FALSE)
) %>%
dplyr::pull(test) %>%
any(na.rm = TRUE)
(I realize that it relies on dplyr, lubridate, and tibble, but since those are all already loaded in fable.bsts it isn't additional overhead. I simply haven't thought through a method using base R or just lubridate)
I've found a couple of series that can be modeled with only a monthly-annual cycle component and as little as 34 days, but a series with too few days seems to get stuck in a loop. The following example hangs when trying to model 60 days.
library(bsts)
data <- zoo(runif(100, 0, 10), seq.Date(from = as.Date("2020-01-01"), by = 1, length.out = 100))
for (i in length(data):1) { print(i) state <- AddMonthlyAnnualCycle(state.specification = list(), y = data[1:i]) bsts(data[1:i], state.specification = state, niter = 10, ping = 0) } It could be as simple as a test for length of y in the AddMonthlyAnnualCycle() function, but because I've found spots where it didn't hang until there were even fewer observations, I'm unsure. I assume it might be a matter of needing to have the entirety of at least one full calendar month, with a day on either side? If so, the test would need to be for that.
After testing, it looks like it does require a full calendar month and one day before and after to that calendar month. The following runs:
library(bsts) data <- zoo(runif(100, 0, 10), seq.Date(from = as.Date("2019-12-31"), by = 1, length.out = 33)) state <- AddMonthlyAnnualCycle(state.specification = list(), y = data) bsts(data, state.specification = state, niter = 10, ping = 0) Shortening the data by one observation on either end will cause it to hang. The following logical statement tests this on a tibble of data:
!tbl_data %>% dplyr::count(month = lubridate::floor_date(index, unit = "month")) %>% dplyr::mutate( days_in_month = lubridate::days_in_month(month), test = ifelse(n == days_in_month & dply::lag(n) > 0 & dply::lead(n) > 0, TRUE, FALSE) ) %>% dplyr::pull(test) %>% any(na.rm = TRUE) (I realize that it relies on dplyr, lubridate, and tibble, but since those are all already loaded in fable.bsts it isn't additional overhead. I simply haven't thought through a method using base R or just lubridate)