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jingw2
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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 \ alt text \ MQ-RNN \ alt text \ Deep Factors \ alt text \ TPA-LSTM \ alt text

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 training in deep factors/deepar, default 100|

TO DO

  • [X] Deep Factor Model
  • [X] TPA-LSTM pytorch
  • [ ] LSTNet pytorch
  • [ ] Debug Uber Extreme forcaster
  • [ ] Modeling Extreme Events in TS
  • [X] Intermittent Demand Forecasting
  • [ ] Model API

Demand Forecast Dataset Resources

Reference

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