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

Collection of follow-ups to #5827. These can/should be broken out into individual PRs. Many are relatively straightforward and would make a good first PR.

General

• Documentation (none was added in original PR).
• Release notes.
• Example notebook.
• Double-check how sm.tsa.arima.ARIMA works with fix_params (it should fail except when the fit method is statespace

The following estimators fail the extended test_constructor from alan-turing-institute/sktime#2732 and need to be fixed, along the lines of the previously undetected bug #2730. Each fix can be modelled upon #2731.

• ARIMA alan-turing-institute/sktime#2731
• AutoARIMA
• AutoETS https://github.com/alan-turing-institute/sktime/

Is your feature request related to a current problem? Please describe.
Yes. Currently using the "u8darts[torch]" version (for all the chaos for installing prophet but that's another story), every time I run my program with darts imported, I would received the warnings from darts.models.init.py.

It would not be a

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.
Now, the cross_validation method receives test_size, but it is unintuitive. I would like to have n_windows.

Describe the solution you'd like
Preserve test_size while adding n_windows and step_size. Both parameters should be mutually exclusive.

Describe alternatives you've considered
Since `n_windows = t

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     |
------------------------------------------------------