Update README.md

README.md

@@ -15,16 +15,21 @@ can be easily fine-tuned for your target data. Refer to our [paper](https://arxi
 
 ## Benchmark Highlights:
 
-- TTM outperforms pre-trained *GPT4TS (NeurIPS 23) by 7-12% in few-shot forecasting*.
-- TTM outperforms pre-trained *LLMTime (NeurIPS 23) by 24% in zero-shot forecasting*.
-- TTM outperforms pre-trained *SimMTM (NeurIPS 23) by 17% in few-shot forecasting*.
-- Zero-shot results of TTM often surpass the *few-shot results of many SOTA approaches* including
+- TTM (with less than 1 Million parameters) outperforms the following popular Pre-trained SOTAs demanding several hundred Million to Billions of parameters
+  - *GPT4TS (NeurIPS 23) by 12% in few-shot (5%) forecasting.*
+  - *LLMTime (NeurIPS 23) by 24% in zero-shot forecasting*.
+  - *SimMTM (NeurIPS 23) by 17% in few-shot forecasting*.
+  - *Time-LLM (ICLR 24) by 8% in few-shot (5%) forecasting*
+  - *UniTime (WWW 24) by 27% in zero-shot forecasting.*
+  - 
+- Zero-shot results of TTM surpass the *few-shot results of many popular SOTA approaches* including
   PatchTST (ICLR 23), PatchTSMixer (KDD 23), TimesNet (ICLR 23), DLinear (AAAI 23) and FEDFormer (ICML 22).
-- TTM (1024-96, released in this model card) also outperforms *pre-trained MOIRAI* on FL = 96 by ...
+- TTM (1024-96, released in this model card with 1M parameters) outperforms pre-trained MOIRAI (Small, 14M parameters) by 10%, MOIRAI (Base, 91M parameters) by 4% and
+  MOIRAI (Large, 311M parameters) by 3% on forecast length 96.
 - TTM quick fine-tuning also outperforms the hard statistical baselines (Statistical ensemble and S-Naive) in
   M4-hourly dataset which pretrained TS models are finding hard to outperform.
 - TTM takes only a *few seconds for zeroshot/inference* and a *few minutes for finetuning* in 1 GPU machine, as
-  opposed to long timing-requirements and heavy computing infra needs of other pretrained models.
+  opposed to long timing-requirements and heavy computing infra needs of other existing pretrained models.
   
 
 ## Model Description
@@ -74,8 +79,7 @@ Stay tuned for these extended features.
 1. Users have to standard scale their data before feeding it to the model (Refer to TSP, our data processing utility for data scaling.)
 2. Enabling any upsampling or prepending zeros to virtually increase the context length is not recommended and will
    impact the model performance.
-
-
+ 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->