JellalYu/DeepAR

Implementation of DeepAR in PyTorch.

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

Reimplementation of the DeepAR paper(https://arxiv.org/abs/1704.04110) in PyTorch.

Authors:

• Yunkai Zhang(yunkai_zhang@ucsb.edu) - University of California, Santa Barbara

• Qiao Jiang - Brown University

To run:

0. Install all dependencies listed in requirements.txt. Note that the model has only been tested in the versions shown in the text file.
1. Download the dataset and preprocess the data:

python preprocess_elect.py

2. Start training:

python train.py

If you want to perform ancestral sampling,

python train.py --sampling

If you do not want to do normalization during evaluation,

python train.py --relative-metrics

3. Evaluate a set of saved model weights:

python evaluate.py

4. Perform hyperparameter search:

python search_params.py

Results