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Pytorch Implementation of DeepAR, MQ-RNN, Deep Factor Models and TPA-LSTM

Pytorch Implementation of DeepAR, MQ-RNN, Deep Factor Models and TPA-LSTM

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
deepar
MQ-RNN
mq_rnn
Deep Factors
deep_factors
TPA-LSTM
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
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

GitHub

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