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Silencing Warning and printing #62
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Hi, you can use the warnings package to shut off warnings: import warnings
warnings.simplefilter("ignore") But the warnings you are seeing are because of the package not being able to associate seasonalities consistently with the dates you have passed to the object. I think part of the problem may be the repeated use of The repated use of For progression bars, use the from tqdm import tqdm
for i in tqdm(some_loop):
do_something(i) If you are using notebook, you can call Finally, I know you didn't ask about it, but to increase modeling accuracy, I recommend series transformations and pipelines. Optimal series transformations can be found with this function. More than anything, transformations are emerging as the number 1 way to juice forecasting prowess according to the most recent research, at least from what I'm seeing. Please follow up with additional questions. Thanks for raising the issue! |
Thank you for the feedback. I see it more clear now. So, if I understand also well, If the auto Xvar select is called, there is no need to add before the AR terms and seasonalities, right ? I think the function you proposed could be very useful. Otherwise, maybe use it in Tune_cast function that take the model lists. I have a question regarding the best series length, is it about determining best training length? I have few other questions, better to add them in another different threads. Thank you again. |
If An alternative method for auto-selecting variables is to add all of them you think you might need and drop them one at a time using I will add the There is a As to your last question, it is about finding the best total length for the series in the object, taking into consideration whatever validation/test sets you also have loaded. It's not any more complicated than trying a bunch of difference lengths and choosing the one where the out-of-sample error/accuracy metric returns the best score. It supports cross-validation as well. Feel free to ask any questions you want in this or other threads. It really helps me improve the package. Thanks! |
- Added the `restore_series_length()` function (#62). - Changed how in-sample metrics are evaluated. If the series length currently in the object and the predictions are different lengths, the prediction-length is truncated so that an in-sample metric can still be evaluated. - Fixed documentation typos.
I added the for m in models:
f.drop_all_Xvars()
f.set_estimator(m)
f.auto_Xvar_select(estimator=m)
f.determine_best_series_length(estimator=m)
f.cross_validate()
f.auto_forecast()
f.restore_series_length() |
Thank you,
|
Yes, please upgrade the package: Other regressors would be any you don't add using the following functions: f.add_ar_terms()
f.add_AR_terms()
f.add_seasonal_regressors()
f.add_time_trend() All of that will be searched for in the |
Are we okay to close this one? |
Hello,
How can I silence some warnings ?
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:1902: Warning: Trend decomposition did not work and raised this error: You must specify a period or x must be a pandas object with a PeriodIndex or a DatetimeIndex with a freq not set to None Switching to the non-decomp method.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:2034: Warning: No seasonalities are currently associated with the None frequency.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:1902: Warning: Trend decomposition did not work and raised this error: You must specify a period or x must be a pandas object with a PeriodIndex or a DatetimeIndex with a freq not set to None Switching to the non-decomp method.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:2034: Warning: No seasonalities are currently associated with the None frequency.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:1902: Warning: Trend decomposition did not work and raised this error: You must specify a period or x must be a pandas object with a PeriodIndex or a DatetimeIndex with a freq not set to None Switching to the non-decomp method.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:2034: Warning: No seasonalities are currently associated with the None frequency.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:1902: Warning: Trend decomposition did not work and raised this error: You must specify a period or x must be a pandas object with a PeriodIndex or a DatetimeIndex with a freq not set to None Switching to the non-decomp method.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:2034: Warning: No seasonalities are currently associated with the None frequency.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:1902: Warning: Trend decomposition did not work and raised this error: You must specify a period or x must be a pandas object with a PeriodIndex or a DatetimeIndex with a freq not set to None Switching to the non-decomp method.
warnings.warn(
/config/workspace/my_venv/lib/python3.11/site-packages/scalecast/Forecaster.py:2034: Warning: No seasonalities are currently associated with the None frequency.
This is my code:
`
df = load_data(database_filename,FILTERED_ITEM_NUMBER)
`
Also, is there a progression bar ?
Thank you in advance,
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