and wonder if i can get a documentation about those parameters... tried to find it yesterday and could not find it.

We are trying to improve our documentation, and we’d love to receive your feedback on how we can improve it 🙂

after it runs, i try to save it as csv but it errors out. the directory does exists as that's where the import csv is from. thought? help? code: Y_hat_df.to_csv('dbfs:/FileStore/tables/bronze/Core/NIXTLA.csv') error: OSError: Cannot save file into a non-existent directory: 'dbfs:/FileStore/tables/bronze/Core'

will provide more feedback later on when it's completed. thank you very much

Perhaps the error is related to the path, here mentions that the path should have the format

/dbfs/FileStore/...

. Thus maybe changing

'dbfs:/FileStore/tables/bronze/Core/NIXTLA.csv'

to

'/dbfs/FileStore/tables/bronze/Core/NIXTLA.csv'

could work

questions... i ran a few examples and it was successful... but when i ran another batch, it errors out: ZeroDivisionError: division by zero I assume for certain unique_id, the Y is so close to 0 or something else causing it to fail mathematically. How i can track down which ID was running, and how to use soemthing like "Try... except... else" to move on to next ID/forecast and document which one fails?

by the way, happy Friday and thanks!!!

ok... i am changing it to this and running it currently. i feel adding +=1 is small enough to not impacting anything. then i will remove it afterwards.

#adding to remove error constant = 1 products2['y'] += constant #Select SARIMA with seasonality 12 autoARIMA = AutoARIMA(season_length=season_length) # Select ETS with seasonality 12 and multiplicative trend #autoETS = ETS(season_length=12, model='ZMZ') model = StatsForecast(df=products2.set_index('unique_id'), models=[autoARIMA], ##, autoETS freq='MS', n_jobs=-1) #products2 Y_hat_df = model.forecast(horizon).reset_index()

Y_hat_df['AutoARIMA'] -= constant

update, still not working with the same error msg: divided by 0 here is the updated code:

#adding to remove error
constant = 1
products2['y'] = products2['y0'] + constant

init1 = time.time()

#Select SARIMA with seasonality 12
autoARIMA = AutoARIMA(season_length=season_length)

# Select ETS with seasonality 12 and multiplicative trend

model = StatsForecast(df=products2.set_index('unique_id'), 
                      models=[autoARIMA],
                      freq='MS', n_jobs=-1)
init2 = time.time()
Y_hat_df = model.forecast(horizon).reset_index()
end = time.time()

time_model = init2 - init1
time_fcst = end - init2
time_tot = end - init1

#taking constant out
Y_hat_df['AutoARIMA0'] = Y_hat_df['AutoARIMA'] - constant

in the end, Y_hat_df = model.forecast(horizon).reset_index() was highlighted and the error alert was 'divided by 0' error

Follow up: after i removed all the Timers (time.time) and the constant +/-.., it ran thru with no error for the sets of unique ids... very strange 😞

will rerun it later on and see if things happen again.

update: ti divided by 0 does not show up any more. i wonder if it's related to Pandas' version. for some reason, AZ Databricks shows pd on 1.2.4 version. So, i force update it to 1.5.0. with that, all errors are gone.

new update: run it without updating pandas to 1.5.0, it works. i prob need to run it with diff dataset to see how it plays out

@fede (nixtla) (they/them) @Max (Nixtla) - any thoughts? Should I attempt to use the fallback_model as a work around?

@Max (Nixtla) - the

fallback_model

worked for this example. Will be using that moving forward. Is there a way to identify which

unique_id

used the fallback model?

By the way, this is the correct account for @fede (nixtla) (they/them). Let me tag him here.