Awesome
Deep Demand Forecast Models
Pytorch Implementation of DeepAR, MQ-RNN, Deep Factor Models, LSTNet, and TPA-LSTM. Furthermore, combine all these model to deep demand forecast model API.
Requirements
Please install Pytorch before run it, and
pip install -r requirements.txt
Run tests
# DeepAR
python deepar.py -e 100 -spe 3 -nl 1 -l g -not 168 -sp -rt -es 10 -hs 50 -sl 60 -ms
# MQ-RNN
python mq_rnn.py -e 100 -spe 3 -nl 1 -sp -sl 72 -not 168 -rt -ehs 50 -dhs 20 -ss -es 10 -ms
# Deep Factors
python deep_factors.py -e 100 -spe 3 -rt -not 168 -sp -sl 168 -ms
# TPA-LSTM
python tpa_lstm.py -e 1000 -spe 1 -nl 1 -not 168 -sl 30 -sp -rt -max
DeepAR
MQ-RNN
Deep Factors
TPA-LSTM
Arguments
| Arguments | Details |
|---|---|
| -e | number of episodes |
| -spe | steps per episode |
| -sl | sequence length |
| -not | number of observations to train |
| -ms | mean scaler on y |
| -max | max scaler on y |
| -nl | number of layers |
| -l | likelihood to select, "g" or "nb" |
| -rt | run test data |
| -sample_size | sample size to sample after </br> training in deep factors/deepar, default 100 |
TO DO
- Deep Factor Model
- TPA-LSTM pytorch
- LSTNet pytorch
- Debug Uber Extreme forcaster
- Modeling Extreme Events in TS
- Intermittent Demand Forecasting
- Model API