I have created a dummy code here for reproducing the case -
# CREATE DATA SET FOR REPRODUCING THE SCENARIO
databrics_dummy <- data.frame(cal_dt = seq(as.Date("2020-01-01"), as.Date("2021-03-31"), by = "day"),
department = rep("F22", 456),
observed_cleaned = seq(20, 60, length = 456))
# creating group key & index
databrics_dummy_ts <-
databrics_dummy %>%
as_tsibble(index= cal_dt, key = department) %>%
group_by_key()
# impute missing values
databrics_dummy_ts <-
databrics_dummy_ts %>%
fill_gaps() %>%
fill(observed_cleaned, .direction = "down")
# run model
fit_db <-
databrics_dummy_ts %>%
model(
arima = ARIMA(observed_cleaned),
arima_def = ARIMA(observed_cleaned ~ pdq(d=1) + PDQ(D=1)),
ets = ETS(observed_cleaned)
)
# Results
# A mable: 1 x 4
# Key: department [1]
department arima arima_def ets
<chr> <model> <model> <model>
1 F22 <NULL model> <ARIMA(1,1,1)(0,1,1)[7]> <ETS(M,A,M)>
It produces NULL model for auto arima. I am using databricks platform (run it in 8.0 runtime environment).
Environment detail -
platform x86_64-pc-linux-gnu
arch x86_64
os linux-gnu
system x86_64, linux-gnu
status
major 4
minor 0.3
year 2020
month 10
day 10
svn rev 79318
language R
version.string R version 4.0.3 (2020-10-10)
nickname Bunny-Wunnies Freak Out