Currently cutoff dates for cross validation are computed based on the period, initial, and horizon parameters here:
https://github.com/facebook/prophet/blob/master/python/fbprophet/diagnostics.py#L19
https://github.com/facebook/prophet/blob/master/R/R/diagnostics.R#L20

These parameters have to be valid Timedeltas (Py) or difftimes (R), which means they can't be things like "1 month" who

Description

Now that we have distributions like the Beta distribution, it only makes sense to create a Min-Max Scaler to map the input into the right input domain (support) for the distributions.

It would need to ne something like https://gluon-ts.mxnet.io/api/gluonts/gluonts.block.scaler.html?highlight=meanscaler#gluonts.block.scaler.MeanScaler the MeanScaler.

Extend NaiveForecaster to include all common naive forecasting strategies. For an overview, see this chapter.

• introduce seasonal as boolean kwarg, refactor "seasonal_last" and implement "seasonal_mean", so that we can set seasonal=True and strategy="mean" for example
• add "drift" strategy, the forecasts should be

While some methods do validate input data, some take data as-is and can result in runtime errors or unexpected behavior.

Formalizing the restrictions, both general dataset restrictions and valid meter topologies, will allow the addition of an optional validation step for each method.

(Potentially) related issues and comments:

• nilmtk/nilmtk#564 (comment)

Apologies in advance if I'm clogging up the issues page....

The forecast docs don't specify exactly how you obtain a point forecast. I assume that for a median, it would be via forecast(*, level=0.5). Is there anything similar for the mean? The reference to the transformations vignette doesn't really clarify matters, as it doesn't seem to provide an example of using the mean.

Feature request -

Remove NA values for pacf or acf plots.

Helps quickly run commands like TSstudio::ts_acf( diff ( ts.obj ) )

PyAF has a API call lEngine.standardPlots(). It gives some classical plots (signal against forecast, residues, trends, cycles, AR)

All the plots are generated with matplotlib

Document the plots generated.

The REST service (issue #20 ) also gives the same plots in a png/base64 encoding, to be documented.

It would be nice if there was a formal documentation somewhere with a "quick start" etc.

I'm not a Java programmer and other non-java programmers could quickly see how to use the library.

What I mean by documentation is not formal api documentation but rather tutorials and examples.

I would like to use the library for implementing ARIMA models for some data.

tried pip install supplychainpy
and also clone git repo and python3 setup.py install.
Got this error: can't copy 'supplychainpy/reporting/static': doesn't exist or not a regular file

From an older issue
@infotroph about documentation: https://www.divio.com/blog/documentation/

More context: This piece makes a strongly-argued case that there are four distinct types of software documentation, and that all well-documented projects need to have all four of them as explicitly separate sections:

• tutorials, for teaching beginners what your tool does using step-by-step example

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