I have been recreating the plot_component daily and weekly graphs using the forecast dataframe.

forecast = m.predict(future)
fig = m.plot_components(forecast)

Recreating the daily component is straight forward, as it is repeated for every day in the dataset. So can be plotted as:

forecast['daily][:n].plot() 
# Where n = number of readings in 24hr period

However, t

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

• Time series forest

Describe the solution you'd like

• Replace loops with joblib parallelized computations or numba.

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.

Describe proposed solution
New function in TimeSeries class named transform taking as parameter function and applying it to TimeSeries itself. For reference see pandas-pipe

signature:
transform(self, function, *args, **kwargs) -> TimeSeries