Upload README.md

README.md

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+# 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
+```
+
+```
+import torch
+from transformers import AutoModelForCausalLM
+# load pretrain model
+# supports different lookback/forecast lengths
+model = AutoModelForCausalLM.from_pretrained('DecisionIntelligence/LightGTS', trust_remote_code=True) 
+# prepare input
+batch_size, lookback_length = 1, 528
+seqs = torch.randn(batch_size, lookback_length).unsqueeze(-1).float()
+# Note that Sundial can generate multiple probable predictions
+forecast_length = 192
+outputs = model.generate(seqs, patch_len = 48, stride_len=48, max_output_length=forecast_length, inference_patch_len=48)
+print(output.shape) 
+```
+
+
+## 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}
+}
+
+```