update readme

README.md

@@ -6,7 +6,7 @@ Related reading: [Moirai Paper](https://arxiv.org/abs/2402.02592), [Moirai Sales
 
 ## 🎉 What's New
 
-* Oct 2024: A new model Moirai-MoE! The preprint is available on [arXiv](https://arxiv.org/abs/2410.10469), along with model weights of [small](https://huggingface.co/Salesforce/moirai-moe-1.0-R-small) and [base](https://huggingface.co/Salesforce/moirai-moe-1.0-R-base), and [simple example](https://github.com/SalesforceAIResearch/uni2ts/project/moirai-moe-1) to get started.
+* Oct 2024: A new model Moirai-MoE! The preprint is available on [arXiv](https://arxiv.org/abs/2410.10469), along with model weights of [small](https://huggingface.co/Salesforce/moirai-moe-1.0-R-small) and [base](https://huggingface.co/Salesforce/moirai-moe-1.0-R-base), and [simple example](https://github.com/SalesforceAIResearch/uni2ts/tree/main/project/moirai-moe-1) to get started.
 
 * Sep 2024: Released [Evaluation Code](https://github.com/SalesforceAIResearch/uni2ts/tree/main/project/benchmarks) of [TimesFM](https://arxiv.org/abs/2310.10688), [Chronos](https://arxiv.org/abs/2403.07815) and [VisionTS](https://arxiv.org/abs/2408.17253) on Monash, LSF and PF benchmarks.
 

project/moirai-moe-1/README.md

@@ -5,7 +5,7 @@ Our paper [Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixt
 The figure below presents the major difference between Moirai-MoE and Moirai. Compared to Moirai using multi-heuristic-defined input/output projection layers to model time series with different frequencies, Moirai-MoE utilizes a single input/output projection layer while delegating the task of capturing diverse time series patterns to the sparse mixture of experts Transformers. With these designs, the specialization of Moirai-MoE is achieved in a data-driven manner and operates at the token level.
 
 <p align="center">
-<img src="./img/framework.png" height="200" alt="" align=center />
+<img src="./img/framework.png" height="300" alt="" align=center />
 </p>
 
 
@@ -85,13 +85,13 @@ plot_next_multi(
 Extensive experiments on 39 datasets demonstrate the superiority of Moirai-MoE over existing foundation models in both in-distribution and zero-shot scenarios.
 
 <p align="center">
-<img src="./img/in-dist.png" height="250" alt="" align=center />
+<img src="./img/in-dist.png" height="300" alt="" align=center />
 </p>
 
 The above figure presents the in-distribution evaluation using a total of 29 datasets from the Monash benchmark. The evaluation results show that Moirai-MoE beats all competitors.
 
 <p align="center">
-<img src="./img/zero-shot.png" height="410" alt="" align=center />
+<img src="./img/zero-shot.png" height="450" alt="" align=center />
 </p>
 
 The above table shows a zero-shot forecasting evaluation on 10 datasets and Moirai-MoE-Base achieves the best zero-shot performance.