Deep4Cast Documentation¶
Forecasting for decision making under uncertainty¶
This package is under active development. Things may change :-).
Deep4Cast is a scalable machine learning package implemented in Python
and Torch. It has a front-end API similar to scikit-learn. It is
designed for medium to large time series data sets and allows for modeling of
forecast uncertainties.
The network architecture is based on WaveNet. Regularization and
approximate sampling from posterior predictive distributions of forecasts are
achieved via Concrete Dropout.
Examples¶
Authors¶
- Toby Bischoff
- Austin Gross
- Kenneth Tran
References¶
- Concrete Dropout is used for approximate posterior Bayesian inference.
- Wavenet is used as encoder network.