Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

This repository is a forked implementation of Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. The original repository is available at https://github.com/microsoft/StemGNN.

Requirements

Recommended version of Python:

Python: Python 3.8 or higher.

To install Python dependencies, virtualenv or conda environment is recommended. For poetry installation instructions, check this link. Before running the following steps, ensure that your environment is activated.

poetry install

Original Data Description

Datasets

PEMS03, PEMS04, PEMS07, PEMS08, METR-LA, PEMS-BAY, Solar, Electricity, ECG5000, COVID-19

We can get the raw data through the links above. We evaluate the performance of traffic flow forecasting on PEMS03, PEMS07, PEMS08 and traffic speed forecasting on PEMS04, PEMS-BAY and METR-LA. So we use the traffic flow table of PEMS03, PEMS07, PEMS08 and the traffic speed table of PEMS04, PEMS-BAY and METR-LA as our datasets. We download the solar power data of Alabama (Eastern States) and merge the 5-minute csv files (totally 137 time series) as our Solar dataset. We delete the header and index of Electricity file downloaded from the link above as our Electricity dataset. For COVID-19 dataset, the raw data is under the folder csse_covid_19_data/csse_covid_19_time_series/ of the above github link. We use time_series_covid19_confirmed_global.csv to calculate the daily number of newly confirmed infected people from 1/22/2020 to 5/10/2020. The 25 countries we take into consideration are 'US','Canada','Mexico','Russia','UK','Italy','Germany','France','Belarus ','Brazil','Peru','Ecuador','Chile','India','Turkey','Saudi Arabia','Pakistan','Iran','Singapore','Qatar','Bangladesh','Arab','China','Japan','Korea'.

The input csv file should contain no header and its shape should be T*N, where T denotes total number of timestamps, N denotes number of nodes.

Since complex data cleansing is needed on the above datasets provided in the urls before fed into the StemGNN model, we provide a cleaned version of ECG5000 (./dataset/ECG_data.csv) for reproduction convenience. The ECG_data.csv is in shape of 5000*140, where 5000 denotes number of timestamps and 140 denotes total number of nodes. Run command python main.py to trigger training and evaluation on ECG_data.csv.

Training and Evaluation

The training procedure and evaluation procedure are all included in the main.py. To train and evaluate on some dataset, run the following command:

python main.py --train True --evaluate True --dataset <name of csv file> --output_dir <path to output directory> --n_route <number of nodes> --window_size <length of sliding window> --horizon <predict horizon> --norm_method z_score --train_length 7 --validate_length 2 --test_length 1

The detailed descriptions about the parameters are as following:

Parameter name	Description of parameter
train	whether to enable training, default True
evaluate	whether to enable evaluation, default True
dataset	file name of input csv
window_size	length of sliding window, default 12
horizon	predict horizon, default 3
train_length	length of training data, default 7
validate_length	length of validation data, default 2
test_length	length of testing data, default 1
epoch	epoch size during training
lr	learning rate
multi_layer	hyper parameter of STemGNN which controls the parameter number of hidden layers, default 5
device	device that the code works on, 'cpu' or 'cuda:x'
validate_freq	frequency of validation
batch_size	batch size
norm_method	method for normalization, 'z_score' or 'min_max'
early_stop	whether to enable early stop, default False

Table 1 Configurations for all datasets

Dataset	train	evaluate	node_cnt	window_size	horizon	norm_method
METR-LA	True	True	207	12	3	z_score
PEMS-BAY	True	True	325	12	3	z_score
PEMS03	True	True	358	12	3	z_score
PEMS04	True	True	307	12	3	z_score
PEMS07	True	True	228	12	3	z_score
PEMS08	True	True	170	12	3	z_score
COVID-19	True	True	25	28	28	z_score

Results

Our model achieves the following performance on the 10 datasets:

Table 2 (predict horizon: 3 steps)

Dataset	MAE	RMSE	MAPE(%)
METR-LA	2.56	5.06	6.46
PEMS-BAY	1.23	2.48	2.63
PEMS03	14.32	21.64	16.24
PEMS04	20.24	32.15	10.03
PEMS07	2.14	4.01	5.01
PEMS08	15.83	24.93	9.26

Table 3 (predict horizon: 28 steps)

Dataset	MAE	RMSE	MAPE
COVID-19	662.24	1023.19	19.3