Update README.md

README.md

@@ -20,7 +20,7 @@ In this paper, we innovatively model time series as a foreign language and const
 
 As depicted in Figure 1(c), during the instruction fine-tuning stage, we fine-tune [ChengsenWang/ChatTime-1-7B-Base](https://huggingface.co/ChengsenWang/ChatTime-1-7B-Base) on [ChengsenWang/ChatTime-1-Finetune-100K](https://huggingface.co/datasets/ChengsenWang/ChatTime-1-Finetune-100K), yielding [ChengsenWang/ChatTime-1-7B-Chat](https://huggingface.co/ChengsenWang/ChatTime-1-7B-Chat).
 
-For details on ChatTime models, training data and procedures, and experimental results, please refer to the [arXiv](https://arxiv.org/abs/0000.00000).
+For details on ChatTime models, training data and procedures, and experimental results, please refer to the [arXiv](https://arxiv.org/abs/2412.11376).
 
 ![](architecture.png)
 
@@ -28,7 +28,7 @@ For details on ChatTime models, training data and procedures, and experimental r
 
 The dataset for instruction fine-tuning is extracted from four task-specific datasets: [text question answering](https://huggingface.co/datasets/tatsu-lab/alpaca), [unimodal time series forecasting](https://huggingface.co/datasets/ChengsenWang/ChatTime-1-Pretrain-1M), [context-guided forecasting](https://huggingface.co/datasets/ChengsenWang/CGTSF), and [time series question answering](https://huggingface.co/datasets/ChengsenWang/TSQA). Each task contributes 25K samples, amounting to a total of 100K fine-tuned instances.
 
-For details on fine-tuning dataset, please refer to the [arXiv](https://arxiv.org/abs/0000.00000).
+For details on fine-tuning dataset, please refer to the [arXiv](https://arxiv.org/abs/2412.11376).
 
 ## 📝 Citation