Štěpán Müller
12/14/2022, 4:24 PMstatsforecast
. Does it take missing values into consideration? From what I have seen so far when testing it, it only seems to use the timestamps of entries for ordering them and not for anything else. At least the cross-validation and the predict method behave this way, I am not sure about the fit method. The cross-validation method seems to iterate over windows of constant size, regardless of how they are placed in time. And the forecast method just forecasts the next K days, regardless of the datetimes specified in the X_df
argument.Maria
12/15/2022, 8:21 AMAndrei Tulbure
12/16/2022, 7:31 AMDarius Marginean
12/16/2022, 8:29 AMcrossvalidation
method from the MLForecast
class it is done using Time Series cross-validation & in statsforecast, the crossvalidation
method from StatsForecast class it is done using Expanding Window?
2. I've walked over your tutorial for crossvalidation
in statsforecast
(https://nixtla.github.io/statsforecast/examples/crossvalidation.html) and when you want to evaluate the results using RMSE from datasetsforecast.losses
, I've seen that it is computed on the whole crossvalidation_df
not on each fold separately & then aggregate the results ( unlike in mlforecast: https://nixtla.github.io/mlforecast/docs/end_to_end_walkthrough.html, where you've put an example on how to compute the losses on each fold separately using evaluate_cf(df):
functionElijas
12/19/2022, 10:00 PMElijas
12/19/2022, 10:08 PMtrain_ds=[2010, 2011, 2014, 2016]
)
I was trying to build an adapter for orbit to be integrated into the nixtla model ecosystem (implement custom adapter class with fit, predict
). However, I can’t seem to find examples where train_ds
is also accessible, not just train_y
.
It seems that the only other option would be to (1) fill in the missing values in y
with NaNs, so that the time series would be uniformly spaced with given freq, say freq='Y'
, or (2) enforce myself to only uniformly spaced data in nixtla libraries (why isn’t this enforced already? Am I missing something)MALISETTY SUMANTH
12/21/2022, 7:06 PMMALISETTY SUMANTH
12/21/2022, 7:07 PMValeriy
12/22/2022, 8:50 AMDarius Marginean
12/22/2022, 5:22 PMdifferences=[]
do we need to do some scaling on data?Mark Aron Szulyovszky
12/23/2022, 12:08 PMMax (Nixtla)
12/23/2022, 10:16 PMValeriy
12/26/2022, 3:30 PMValeriy
12/26/2022, 4:17 PMMax (Nixtla)
12/26/2022, 4:45 PMValeriy
12/26/2022, 5:59 PMArona Ben Cherif DIATTA
12/26/2022, 6:42 PMBoyd
12/28/2022, 2:39 PMBoyd
12/28/2022, 3:46 PMValeriy
12/29/2022, 6:59 PMAndrei Tulbure
01/03/2023, 2:14 PMpy.warnings WARNING /home/andreitulbure/anaconda3/envs/python-translation/lib/python3.10/site-packages/statsforecast/ets.py:1141: UserWarning: I can't handle data with frequency greater than 24. Seasonality will be ignored.
Chris Gervais
01/04/2023, 3:54 PMneuralforecast
accept a tz-aware ds
column!? it seems to work locally but has anyone tried this?Andrei Tulbure
01/04/2023, 9:47 PMChris Gervais
01/04/2023, 3:55 PMJose Bordon
01/09/2023, 6:57 PMJose Bordon
01/09/2023, 6:57 PMFarzad E
01/11/2023, 10:02 PMFarzad E
01/11/2023, 10:42 PMJose Bordon
01/12/2023, 6:11 PMDarius Marginean
01/18/2023, 2:26 PM