"Graph Neural Controlled Differential Equations for Traffic Forecasting", AAAI 2022

Related tags

Deep Learning differential-equations spatio-temporal-graph traffic-forecasting
Overview

Graph Neural Controlled Differential Equations for Traffic Forecasting

Setup Python environment for STG-NCDE

Install python environment

$ conda env create -f environment.yml

Reproducibility

Usage

In terminal

  • Run the shell file (at the root of the project)
$ bash run.sh
  • Run the python file (at the model folder)
$ cd model

$ python Run_cde.py --dataset='PEMSD4' --model='GCDE' --model_type='type1' --embed_dim=10 --hid_dim=64 --hid_hid_dim=64 --num_layers=2 --lr_init=0.001 --weight_decay=1e-3 --epochs=200 --tensorboard --comment="" --device=0
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Comments
  • About irregular data

    About irregular data

    Hi, could you please show the code related to irregular data generation? Or, could you describe how you generate the irregular data, please? Thank you.

    question 
    opened by black-tea-26 1
  • Longer time-series input may performs bad?

    Longer time-series input may performs bad?

    Congratudate for your AAAI 2022 work. It's a nice work, but i met some problem when i use your model as a sota in my experiment. It performed very bad when i try to input 16, 24 or 144 steps in electricity and bikeNYC dataset. Even though i try to adjust some parameters like node_embedd dim, Chenb_k (1,2,3), hidden channel (16 may be the best), loss function (L2 and L1), it stilll does not make. I would like to inform you for some good solutions to solve it. I will appreciate it very much if there are some replies.

    discussion 
    opened by lixus7 2
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Jeongwhan Choi
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Jeongwhan Choi
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