I'm trying to compare our hand-rolled cross-validation methods (which uses

sktime

) with the

neuralforecast

native cross-validation but we're running into a merge issue. The culprit seems to be in the core

NeuralForecast

class here:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[56], line 1
----> 1 nf.cross_validation(df=df, static_df=static_df, val_size=val_size)

File ~/Desktop/rtobots/.venv/lib/python3.9/site-packages/neuralforecast/core.py:386, in NeuralForecast.cross_validation(self, df, static_df, n_windows, step_size, val_size, test_size, sort_df, verbose, **data_kwargs)
    383 fcsts_df = pd.concat([fcsts_df, fcsts], axis=1)
    385 # Add original input df's y to forecasts DataFrame
--> 386 fcsts_df = fcsts_df.merge(df, how="left", on=["unique_id", "ds"])
    387 return fcsts_df

and the actual error appears to be related to index types that don't match:

ValueError: You are trying to merge on datetime64[ns] and object columns. If you wish to proceed you should use pd.concat

Replication: • using the exogenous tutorial here https://nixtla.github.io/neuralforecast/examples/exogenous_variables.html • replacing the

.fit()

code chunk with

.cross_validation(df=df, static_df=static_df, val_size=int(len(df) * .2))

Hi @Chris Gervais I think this is a common error where the

ds

columns do not match in their types. You would need to convert one of them using

<http://pd.to|pd.to>_datetime

.

ahh yep, sorry about that 👍 i dropped the conversion to troubleshoot some tz stuff and forgot to add it back in. thank you!

@Chris Gervais, I am unfamiliar of tz-aware timestamps it might depend on the pandas versions that we currently have, in particular it would depend on wether pandas has updated the methods we are using within the library for tz.

i gave it a try, the normal

.fit()

method works but

.cross_validation()

fails, i think it's when merging the cv forecast dataframes. i can toss up an issue with replication steps if that's helpful?

Sounds good

on a separate note, are there any examples of passing

pl.Trainer

kwargs down from either the core

NeuralForecast

class or the individual models, ie

NHITS

?