| --- |
| pipeline_tag: time-series-forecasting |
| tags: |
| - time series |
| - time series foundation models |
| - time series forecasting |
| - zero-shot |
| --- |
| |
|
|
| # LightGTS: A Lightweight General Time Series Forecasting Model |
|
|
| 🚩 **News (2025.06)** LightGTS has been accepted as **ICML 2025**. |
|
|
|
|
|
|
| ## Introduction |
|
|
| <div style="text-align: center;"> |
| <img src="framework.png" alt="LightGTS" style="zoom:80%;" /> |
| </div> |
| |
|
|
| ## Quick Demos |
| ``` |
| pip install transformers==4.30.2 # Use this version for stable compatibility |
| ``` |
|
|
| ### Zero-Shot |
| ``` |
| from configuration_LightGTS import LightGTSConfig |
| from modeling_LightGTS import LightGTSForPrediction |
| import torch |
| from transformers import AutoModelForCausalLM |
| from transformers import AutoModelForCausalLM, MODEL_MAPPING |
| from transformers import AutoConfig |
| # load pretrain model |
| LightGTS_config = LightGTSConfig(context_points=528, c_in=1, target_dim=192, patch_len=48, stride=48) |
| LightGTS_config.save_pretrained("LightGTS-huggingface") |
| |
| AutoConfig.register("LightGTS",LightGTSConfig) |
| AutoModelForCausalLM.register(LightGTSConfig, LightGTSForPrediction) |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "./LightGTS-huggingface", |
| trust_remote_code=True |
| ) |
| # prepare input |
| batch_size, lookback_length = 1, 576 |
| seqs = torch.randn(batch_size, lookback_length).unsqueeze(-1).float() |
| # generate forecasting results |
| forecast_length = 192 |
| outputs = model.generate(seqs, patch_len = 48, stride_len=48, max_output_length=forecast_length, inference_patch_len=48) |
| print(outputs.shape) |
| |
| ``` |
|
|
| ### Fine-tune |
| For usage examples, please see test_finetune.py |
| |
| ## Citation |
| |
| If you find Sundial helpful for your research, please cite our paper: |
| ``` |
| @article{wang2025lightgts, |
| title={LightGTS: A Lightweight General Time Series Forecasting Model}, |
| author={Wang, Yihang and Qiu, Yuying and Chen, Peng and Shu, Yang and Rao, Zhongwen and Pan, Lujia and Yang, Bin and Guo, Chenjuan}, |
| journal={arXiv preprint arXiv:2506.06005}, |
| year={2025} |
| } |
| |
| ``` |
