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UniTime

This is the official repository of our WWW 2024 paper UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting. UniTime aims to explore the potential of utilizing a unified model for generalization across time series application domains. This code base offers an implementation to facilitate cross-domain time series learning research.

Getting Started

Requirements

Our experimental environments include Python 3.9, Pytorch 1.13.1 with CUDA 11.6, and transformers 4.31.0. To install all dependencies, please use the below command.

pip install -r requirements.txt

Datasets

The pre-processed datasets can be obtained from the link here. Then you may choose to download all_datasets.zip, place this zip file into the dataset folder, and finally unzip the file.

Running

In general, we use a csv file to indicate the executing tasks (including training and evaluations) during the cross-domain learning process. There are five columns in the file.

(1) Data: the name of a dataset, corresponding to a config file in the folder data_configs.

(2) Prediction: the prediction length.

(3) Train: the indicator for training.

(4) Valid: the indicator for validation.

(5) Test: the indicator for testing.

For example, the below command is used to train one model for the tasks listed in the file execute_list/train_all.csv. Note that the argument max_token_num should be set to a value larger than the combined number of tokens in both language instructions and time series patches.

python run.py --gpu 0 --training_list execute_list/train_all.csv --max_token_num 17

In the case of evaluating the pretrained model, please setting the argument is_training to 0 and specifying the inference tasks via inference_list.

python run.py --gpu 0 --training_list execute_list/train_all.csv --max_token_num 17 --is_training 0 --inference_list execute_list/inference_all.csv

Citation

If you find our work useful in your research, please cite:

@inproceedings{liu2024unitime,
  title={UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting},
  author={Liu, Xu and Hu, Junfeng and Li, Yuan and Diao, Shizhe and Liang, Yuxuan and Hooi, Bryan and Zimmermann, Roger},
  booktitle={Proceedings of the ACM Web Conference 2024},
  year={2024}
}

Acknowledgement

We appreciate the following github repository for sharing the valuable code base and datasets:

https://github.com/thuml/Time-Series-Library