feat: first commit

README.md

@@ -37,9 +37,47 @@ In this work, we pretrain Aurora in a cross-modality paradigm, which adopts Chan
 <div align="center">
 <img alt="intro" src="https://cdn-uploads.huggingface.co/production/uploads/66276727368ec2a0b933772c/d82jT96jiGD0QL9s8RYg-.png" width="100%"/>
 </div>
-
 ## Quickstart
 
+#### From pypi (recommended)
+
+We have publised Aurora on PyPi, **you can directly install it with one line of code!**
+
+```shell
+$ pip install aurora-model==0.1.0
+```
+
+Then you can use the Aurora model to make zero-shot probabilistic forecasting!
+
+```python
+from aurora import load_model
+import os
+# os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+model = load_model()
+
+# prepare input
+batch_size, lookback_length = 1, 528 
+seqs = torch.randn(batch_size, lookback_length)
+
+# Note that Sundial can generate multiple probable predictions
+forecast_length = 96 
+num_samples = 100
+
+
+# For inference_token_len, you can refer to LightGTS (Periodic Patching).
+# We recommend to use the period length as the inference_token_len.
+output = model.generate(inputs=seqs, max_output_length=forecast_length, num_samples=num_samples, inference_token_len=48)
+
+
+# use raw predictions for mean/quantiles/confidence-interval estimation
+print(output.shape) 
+
+```
+
+
+
+#### From raw code
+
 We release the original code of Aurora in this repo. You can also download the pretrained checkpoints in our [huggingface](https://huggingface.co/DecisionIntelligence/Aurora) repo and put them in the folder: aurora/.
 
 If you want to pretrain an Aurora on your own time series corpus, you need to install the following important packages:
@@ -100,10 +138,11 @@ seqs = torch.randn(batch_size, lookback_length)
 
 # Note that Sundial can generate multiple probable predictions
 forecast_length = 96 
-num_samples = 20
+num_samples = 100
 
 
 # For inference_token_len, you can refer to LightGTS (Periodic Patching).
+# We recommend to use the period length as the inference_token_len.
 output = model.generate(inputs=seqs, max_output_length=forecast_length, num_samples=num_samples, inference_token_len=48)