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Home Page: https://neuralprophet.com
License: MIT License
NeuralProphet: A simple forecasting package
Home Page: https://neuralprophet.com
License: MIT License
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:
might be good to make all inputs 3D, including time.
figsize
parameter for plot_components
has default value of None
.
Add documentation for default behaviour.
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.
plot_components
defaults to None
plot
defaults to (10,6)
Update logging to mention NeuralProphet.
Current behaviour logs "Disabling daily seasonality. Run prophet with daily_seasonality=True to override this."
neural_prophet/neuralprophet/utils.py
Line 153 in af943a2
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
Prophet documentation: https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html
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:
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