This repository provides the minimal code for running inference of GTT.

Getting Started

Install dependencies (with python 3.10)

pip install -r requirements.txt

Run Experiments

Run the zero-shot experiments

cd src
python test_zeroshot.py --gpu [GPUs] --batch_size [BS] --mode [mode] --data [DS] --uni [uni]

Specify mode to one of the following: tiny, small, large.

Specify data to one of the following: m1, m2, h1, h2, electricity, weather, traffic, ill.

Specify uni to 0 or 1, 0: multivariate forecast, 1: univariate forecast

Run the fine-tune experiments

cd experiments
python test_finetune.py --gpu [GPUs] --batch_size [BS] --mode [mode] --data [DS] --uni [uni] --epochs [eps]

Use GTT models for zero-shot forecast on your own data

It is rather straightforward to use GTT models for zero-shot forecast on your own data (even with only CPUs), check the tutorial.

Cite

Cheng Feng, Long Huang, and Denis Krompass. 2024. General Time Transformer: an Encoder-only Foundation Model for Zero-Shot Multivariate Time Series Forecasting. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24), October 21–25, 2024, Boise, ID, USA. ACM, New York, NY, USA, 5 pages. https://doi.org/10.1145/3627673.3679931