J T
10/06/2022, 9:20 PMfede (nixtla) (they/them)
10/06/2022, 9:25 PMfreq='MS'
since your original ds
encodes monthly frequency at the starting date.
Regarding the forecast, would it be possible for you to share your data? Even a time series (unique_id==0
) would be helpful for us to see if there’s a bug somewhere.J T
10/06/2022, 9:28 PMfede (nixtla) (they/them)
10/06/2022, 9:33 PMfreq
parameter, there is a link to the available frequencies (from pandas, https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases).J T
10/07/2022, 1:49 AMfede (nixtla) (they/them)
10/07/2022, 2:11 AM/dbfs/FileStore/...
. Thus maybe changing 'dbfs:/FileStore/tables/bronze/Core/NIXTLA.csv'
to '/dbfs/FileStore/tables/bronze/Core/NIXTLA.csv'
could workJ T
10/07/2022, 6:56 PMfede (nixtla) (they/them)
10/07/2022, 7:15 PMY_df['y'] += constant
Y_hat_df['AutoARIMA'] -= constant
J T
10/07/2022, 9:36 PMY_hat_df['AutoARIMA'] -= constant
#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
mike
10/14/2022, 9:04 PMJ T
10/16/2022, 8:37 PMfede (nixtla) (they/them)
10/17/2022, 4:58 PMmike
10/18/2022, 5:57 PMMax (Nixtla)
10/31/2022, 2:00 AMmike
10/31/2022, 2:13 AMfallback_model
worked for this example. Will be using that moving forward. Is there a way to identify which unique_id
used the fallback model?Max (Nixtla)
11/01/2022, 4:43 PMmike
11/01/2022, 4:57 PMMax (Nixtla)
11/01/2022, 4:59 PM