GridTST

May 24, 2024 · View on GitHub

A simple yet strong Long-term Time Series prediction model.

Model	GridTST	PatchTST (ICLR 2023)	iTransformer (ICLR 2024)	Dlinear (AAAI 2023)
Weather	0.223	0.228	0.236	0.246
Traffic	0.372	0.396	0.386	0.433
Electricity	0.152	0.163	0.165	0.166
Illness	1.649	1.806	2.122	2.169
Etth1	0.416	0.421	0.450	0.422
Ettm1	0.345	0.351	0.365	0.357
Solar	0.187	0.215	0.215	0.244

Requirements

We recommand to use Conda to mange a virtual environment:

conda create -n gridtst python=3.8 && conda activate gridtst
pip install -r requirements.txt

logging and multi-gpu training setup:

wandb login
accelerate config

This is the dataset we use, you could download here and put all csv files in the dataset folder.

Datast	# Channels	# TimeSteps	Prediction Length	Information
Weather	21	52696	{96,192,336,720}	Weather
Traffic	862	17544	{96,192,336,720}	Transportation
Electricity	321	26304	{96,192,336,720}	Electricity
Illness	7	966	{12,24,48,60}	Illness
Etth1	7	17420	{96,192,336,720}	Electricity
Ettm1	7	69680	{96,192,336,720}	Electricity
Solar	137	52560	{96,192,336,720}	Energy

We provide all the scripts on the scripts folder. For example, training on the Weather dataset with lookback window = 336:

bash scripts/lookback_window_336/weather.sh

We provide our trained model on the huggingface space

To evaluate these models, you could either specify a perticular model or evaluate them all at once.

For a certain model, for example GridTST on traffic dataset with lookback window=336 and prediction length=96:

python benchmark.py --data_file dataset/traffic.csv --seq_len 336 --label_len 96

To evaluate them all:

python benchmark.py --all