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 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

Demand Forecast Dataset Resources

• Solar Energy
• Traffic
• Electricity
• MuseDATA

Reference

• DeepAR
• MQ-RNN
• Deep Factor
• LSTNet
• TPA-LSTM
• LSTNet Github
• TPA-LSTM Github
• Uber Extreme Event Forecast 1
• Uber Extreme Event Forecast 2
• Modeling Extreme Events in Time Series Prediction