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# neural-forecast
s
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k
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
.
c
ahh yep, sorry about that 👍 i dropped the conversion to troubleshoot some tz stuff and forgot to add it back in. thank you!
circling back on this, it was actually a tz-aware error:
Copy code
ValueError: You are trying to merge on datetime64[ns] and datetime64[ns, tzfile('/usr/share/zoneinfo/US/Eastern')] columns. If you wish to proceed you should use pd.concat
@Kin Gtz. Olivares do you know if tz-aware timestamps have been tested with 
.cross_validation()
?
k
@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 would try them and see if the result seems reasonable
c
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?
k
Sounds good
c
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
?
trying to get some more control over the trainer kwargs directly: 
pl.Trainer(log_dir=..., auto_lr_find=...)
circling back on the trainer stuff, it's a bit hacky but we found the easiest way to get a good starting LR was to: • instantiate the model class (NHiTS, NBEATsx etc) with 
max_steps=1
• init the 
NeuralForecast
class and run 
.fit()
• deepcopy 
nf.models[0].trainer.datamodule
• init your own trainer class and train with 
pl.Trainer(...).fit(nf.models[0], datamodule=datamodule)
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