Update README.md

README.md

@@ -21,7 +21,7 @@ TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Serie
 
 TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight 
 forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be 
-fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our [paper](https://arxiv.org/pdf/2401.03955v5.pdf) for more details.
+fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf) for more details.
 
 
 **The current open-source version supports point forecasting use-cases specifically ranging from minutely to hourly resolutions 
@@ -30,9 +30,47 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
 **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
 
 
-**Recent updates:** We have developed more sophisticated variants of TTMs (TTM-B, TTM-E and TTM-A), featuring extended benchmarks that compare them with some of the latest models 
-such as TimesFM, Moirai, Chronos, Lag-llama, and Moment. For full details, please refer to the latest version of our [paper](https://arxiv.org/pdf/2401.03955.pdf). 
-Stay tuned for the release of the model weights for these newer variants.
+
+## Model Releases (along with the branch name where the models are stored):
+
+
+- **512-96:** Given the last 512 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
+ in future. This model is targeted towards a forecasting setting of context length 512 and forecast length 96 and
+ recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: main) 
+
+- **1024-96:** Given the last 1024 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
+ in future. This model is targeted towards a long forecasting setting of context length 1024 and forecast length 96 and
+ recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 1024-96-v1) 
+
+- **New Releases (trained on larger pretraining datasets, released on October 2024)**:
+
+ - **512-96-r2**: Given the last 512 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
+ in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
+ resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 512-96-r2)
+
+ - **1024-96-r2**: Given the last 1024 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
+ in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
+ resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 1024-96-r2)
+ 
+
+ - **1536-96-r2**: Given the last 1536 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
+ in future. This model is pre-trained with a larger pretraining dataset for improved accuracy. Recommended for hourly and minutely
+ resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 1536-96-r2)
+ 
+
+## Model Capabilities
+- Zeroshot Multivariate Forecasting
+- Finetuned Multivariate Forecasting:
+ - Channel-Independent Finetuning
+ - Channel-Mix Finetuning
+- **New Releases (extended features released on October 2024)**
+ - Finetuning and Forecasting with Exogenous/Control Variables
+ - Finetuning and Forecasting with static categorical features
+ - Rolling Forecasts - Extend forecast lengths beyond 96 via rolling capability
+ - 
+ 
+ 
+
 
 ## How to Get Started with the Model
 
@@ -84,17 +122,6 @@ only 3-6 hours using 6 A100 GPUs, as opposed to several days or weeks in traditi
 Each pre-trained model will be released in a different branch name in this model card. Kindly access the required model using our 
 getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb) mentioning the branch name.
 
-## Model Releases (along with the branch name where the models are stored):
-
-- **512-96:** Given the last 512 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
- in future. This model is targeted towards a forecasting setting of context length 512 and forecast length 96 and
- recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: main) 
-
-- **1024-96:** Given the last 1024 time-points (i.e. context length), this model can forecast up to next 96 time-points (i.e. forecast length)
- in future. This model is targeted towards a long forecasting setting of context length 1024 and forecast length 96 and
- recommended for hourly and minutely resolutions (Ex. 10 min, 15 min, 1 hour, etc). (branch name: 1024-96-v1) 
-
-- Stay tuned for more models !
 
 ## Model Details