This project implements the Evolutionary State Graph Neural Network proposed in [1], which is a GNN-based method for time-series event prediction.
Code is compatible with tensorflow version 1.2.0 and Pyhton 3.6.2.
Some Python module dependencies are listed in requirements.txt, which can be easily installed with pip:
pip install -r requirements.txt
An example data format is given where data is stored as a list containing 4 dimensionals tensors such as
[number of samples × segment number × segment length × dimension of observation]
We can use ./model_core/config.py to set the parameters of model.
class ModelParam(object):
# basic
model_save_path = "./model"
n_jobs = os.cpu_count()
# dataset
data_path = './data'
data_name = 'webtraffic'
his_len = 15
segment_len = 24
segment_dim = 2
n_event = 2
norm = True
# state recognition
n_state = 30
covariance_type = 'diag'
# model
graph_dim = 256
node_dim = 96
learning_rate = 0.001
batch_size = 1000
id_gpu = '0'
pos_weight = 1.0
python run.py -h
usage: run.py [-h] [-d {djia30, webtraffic}] [-g GPU]
optional arguments:
-h, --help show this help message and exit
-d {djia30,webtraffic}, --dataset {djia30,webtraffic} select the dataset
-g GPU, --gpu GPU target gpu id
[1] Wenjie, H; Yang, Y; Ziqiang, C; Carl, Y and Xiang, R, 2021, Time-Series Event Prediction with Evolutionary State Graph, In WSDM, 2021
@inproceedings{hu2021evonet,
title={Time-Series Event Prediction with Evolutionary State Graph},
author={Wenjie Hu and Yang Yang and Ziqiang Cheng and Carl Yang and Xiang Ren},
booktitle={Proceedings of WSDM},
year={2021}
}