Update README.md

README.md

@@ -8,9 +8,9 @@ pipeline_tag: time-series-forecasting
 
 Large time-series model introduced in this [paper](https://arxiv.org/abs/2402.02368) and enhanced with our [further work](https://arxiv.org/abs/2410.04803).
 
-This version is pre-trained on **307B** time points, which supports zero-shot forecasting and further adaptation. 
-* Zero-shot forecasting benchmarks: [TSLib Dataset](https://cdn-uploads.huggingface.co/production/uploads/64fbe24a2d20ced4e91de38a/n2IW7fTRpuZFMYoPr1h4O.png), [GIFT-Eval]().
-* Codebase for fine-tuning: [Large-Time-Series-Model](https://github.com/thuml/Large-Time-Series-Model).
+This version is pre-trained on **307B** time points with **84M** parameters, a lightweight generative Transformer with the state-of-the-art performance on zero-shot forecasting:
+
+We evaluate the model on the following benchmarks: [TSLib Dataset](), [GIFT-Eval]().
 
 # Quickstart
 
@@ -28,7 +28,9 @@ normed_seqs = (seqs - mean) / std
 
 # forecast
 prediction_length = 96
-output = model.generate(normed_seqs, max_new_tokens=prediction_length)[:, -prediction_length:]
+normed_output = model.generate(normed_seqs, max_new_tokens=prediction_length)[:, -prediction_length:]
+output = std * normed_output + mean
+
 print(output.shape)
 ```