forecasting

make_future_dataframe doesn't support regressors currently. So code like:

m = Prophet()
m.add_regressor('var')
m.fit(df)
forecasts = m.predict(m.make_future_dataframe(periods=7))

gives an error like:

ValueError: Regressor 'var' missing from dataframe when attempting to generate forecasts

I know prophet may not know what exact values to put for var in each of the rows a

@fkiraly update after discussion below (Apr 22):

The API reference page on the sktime.utils module is incorrect:
https://www.sktime.org/en/stable/api_reference/utils.html

Most of the functions aren't even there anymore.
This needs to be updated, preferably urgently.

What should be in the utilities section:

• sktime.datatypes functionality convert, convert_to, check_is, `che

The parameters of the likelihood model assume invalid values during training.

Description

(A clear and concise description of what the feature is.)

• util.cumsum implementation https://github.com/awslabs/gluon-ts/blob/master/src/gluonts/mx/util.py#L326 does not scale under mx.ndarray
• cumsum is 2-5 times slower than nd.cumsum under both mx.sym and mx.ndarray, and even fails for large 4-dim input

Sample test

Code

# import ...
def test_

Great first issue.

After installing NeuralProphet as developer (see CONTRIBUTING) - run pytest -v and see warning messages

Addressing these will prevent warnings becoming errors.

Is your feature request related to a problem? Please describe.

It would be nice to directly support simulating from a fitted ARIMA model, e.g. to have a simulate method to call that would delegate to statsmodels.tsa.arima.model.ARIMA.simulate. Right now, the only way I found is to use arima_res_ member of the fitted object.

Describe the solution you'd like

Class `pmdarima.arima.ar

We have a lot of antiquated docstrings that don't render well into ReadTheDocs. A kind of grunge (but incredibly useful) task would be to refactor these docstrings into proper ReadTheDocs format. This would allow us to render them effectively...

Is your feature request related to a problem? Please describe.
The library already has the AutoARIMA class but it would be helpful to have the ARIMA class.

Describe the solution you'd like
An ARIMA class.

right now the mase (numpy version) returns different values than MASELoss (pytorch version)


the numpy version averages both delta_y and scale before dividing, while the pyto

🐛 Bug Report

I ran code from How To Reproduce and got strange results: fold_info column in backtest's forecasts was not added to ts[:, segment] block but concatenated separately from other segment's columns.

Expected behavior

I expected smth like

|segment |     segment_0      |     segment_1      |      segment_2     |
------------------------------------------------------