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

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

Financial modeling usually deals with modeling the volatility of time series data, ARCH, GARCH, etc. are common models used for this purpose.

Describe the solution you'd like

Add support for https://github.com/bashtage/arch (as an adapter) in sktime. This will be very useful for folks in the financial industry.
This

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_

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...

🐛 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     |
------------------------------------------------------

Hello, I think there is an error in the SMAPELoss Numpy implementation.

For the SMAPE function, the NumPy version always gives 100 times the PyTorch result.
Eg:
Numpy: 76.6044944858551
Pytorch: 0.7669

https://github.com/Nixtla/neuralforecast/blob/3248751ccdb1edab2ded9c14d9666eaddd1b1469/neuralforecast/losses/numpy.py#L218-L219

Since the description on the Numpy file was intended to ma