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.