ourownstory / neural_prophet Goto Github PK

Update logging to mention NeuralProphet.

Current behaviour logs "Disabling daily seasonality. Run prophet with daily_seasonality=True to override this."

inspiration
https://github.com/cs230-stanford/cs230-code-examples/tree/master/pytorch

Can simply save/load NeuralProphet.model
https://pytorch.org/tutorials/beginner/saving_loading_models.html

Issue:
must also store hyperparameter settings and any settings for events, regressors, etc.
may need a change in how configurations are stored/initialized

How:
We should implement this using torch.save() and torch.load()
with the recommended way of storing pytorch models
https://stackoverflow.com/questions/42703500/best-way-to-save-a-trained-model-in-pytorch

Title is descriptive.

When using the parameter crop_last_n from plot_components, the Residuals plot is empty and not aligned with other plots.

Needed for better learning of trend deltas without gradient bleedover to older trend delta parameters. This is important if we want to achieve proper automatic trend-changepoint detection.

m.plot_components plots the seasonality for all of the available history.

This can make some plots - like the weekly seasonality - not so easy to understand. See attachment.

According to documentation, crop_last_n, which would help alleviate this issue, should be ignored for seasonality.

We are looking to release a collection of example datasets that are ready to use with NeuralProphet. Of course they must be openly available without conflicting copyrights.

Sources may include:

• http://www.ercot.com/gridinfo/load/load_hist
• https://www.kaggle.com/robikscube/hourly-energy-consumption

might be good to make all inputs 3D, including time.

plot_components defaults to None
plot defaults to (10,6)

inspiration https://github.com/cs230-stanford/cs230-code-examples/tree/master/pytorch

Currently, the best approach is to simply map it to some random series of datestamps.
A helper function to do this might be useful, as dealing with datetimes can be messy.

However, proper support for such data should be implemented eventually. It is not complex, but somewhat complicated as there are many touch points with datetime stamps.
This will entail:

• data checks and missing data imputation
• trend and seasonality computation
• plotting support

Prophet documentation: https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html

figsize parameter for plot_components has default value of None.

Add documentation for default behaviour.