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@@ -13,7 +13,8 @@ TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Serie
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  TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight
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  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
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- 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.
 
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  **The current open-source version supports point forecasting use-cases ranging from minutely to hourly resolutions
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  (Ex. 10 min, 15 min, 1 hour, etc.)**
@@ -21,6 +22,9 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
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  **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
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  ## How to Get Started with the Model
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@@ -33,7 +37,7 @@ fine-tuned for multi-variate forecasts with just 5% of the training data to be c
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  ## Benchmark Highlights:
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- - TTM (with less than 1 Million parameters) outperforms the following popular Pre-trained SOTAs demanding several hundred Million to Billions of parameters [paper](https://arxiv.org/pdf/2401.03955.pdf):
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  - *GPT4TS (NeurIPS 23) by 7-12% in few-shot forecasting*
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  - *LLMTime (NeurIPS 23) by 24% in zero-shot forecasting*.
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  - *SimMTM (NeurIPS 23) by 17% in few-shot forecasting*.
@@ -80,7 +84,7 @@ getting started [notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdem
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  ## Model Details
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- For more details on TTM architecture and benchmarks, refer to our [paper](https://arxiv.org/pdf/2401.03955.pdf).
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  TTM-1 currently supports 2 modes:
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@@ -99,15 +103,16 @@ In addition, TTM also supports exogenous infusion and categorical data which is
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  Stay tuned for these extended features.
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  ## Recommended Use
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- 1. Users have to externally standard scale their data indepedently for every channel before feeding it to the model (Refer to [TSP](https://github.com/IBM/tsfm/blob/main/tsfm_public/toolkit/time_series_preprocessor.py), our data processing utility for data scaling.)
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- 2. Enabling any upsampling or prepending zeros to virtually increase the context length for shorter length datasets is not recommended and will
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  impact the model performance.
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  ### Model Sources
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  - **Repository:** https://github.com/IBM/tsfm/tree/main/tsfm_public/models/tinytimemixer
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- - **Paper:** https://arxiv.org/pdf/2401.03955.pdf
 
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  ## Uses
@@ -173,17 +178,19 @@ work
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  **BibTeX:**
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  ```
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- @article{ekambaram2024ttms,
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- title={TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series},
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- author={Ekambaram, Vijay and Jati, Arindam and Nguyen, Nam H and Dayama, Pankaj and Reddy, Chandra and Gifford, Wesley M and Kalagnanam, Jayant},
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- journal={arXiv preprint arXiv:2401.03955},
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- year={2024}
 
 
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  }
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  ```
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  **APA:**
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- Ekambaram, V., Jati, A., Nguyen, N. H., Dayama, P., Reddy, C., Gifford, W. M., & Kalagnanam, J. (2024). TTMs: Fast Multi-level Tiny Time Mixers for Improved Zero-shot and Few-shot Forecasting of Multivariate Time Series. arXiv preprint arXiv:2401.03955.
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  ## Model Card Authors
 
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  TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight
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  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
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+ 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.
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+
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  **The current open-source version supports point forecasting use-cases ranging from minutely to hourly resolutions
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  (Ex. 10 min, 15 min, 1 hour, etc.)**
 
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  **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed in 1 GPU machine or in laptops too!!**
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+ **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
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+ 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).
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+ Stay tuned for the release of the model weights for these newer variants.
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  ## How to Get Started with the Model
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  ## Benchmark Highlights:
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+ - TTM (with less than 1 Million parameters) outperforms the following popular Pre-trained SOTAs demanding several hundred Million to Billions of parameters [paper](https://arxiv.org/pdf/2401.03955v5.pdf):
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  - *GPT4TS (NeurIPS 23) by 7-12% in few-shot forecasting*
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  - *LLMTime (NeurIPS 23) by 24% in zero-shot forecasting*.
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  - *SimMTM (NeurIPS 23) by 17% in few-shot forecasting*.
 
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  ## Model Details
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+ For more details on TTM architecture and benchmarks, refer to our [paper](https://arxiv.org/pdf/2401.03955v5.pdf).
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  TTM-1 currently supports 2 modes:
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  Stay tuned for these extended features.
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  ## Recommended Use
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+ 1. Users have to externally standard scale their data independently for every channel before feeding it to the model (Refer to [TSP](https://github.com/IBM/tsfm/blob/main/tsfm_public/toolkit/time_series_preprocessor.py), our data processing utility for data scaling.)
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+ 2. Enabling any upsampling or prepending zeros to virtually increase the context length for shorter-length datasets is not recommended and will
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  impact the model performance.
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  ### Model Sources
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  - **Repository:** https://github.com/IBM/tsfm/tree/main/tsfm_public/models/tinytimemixer
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+ - **Paper:** https://arxiv.org/pdf/2401.03955v5.pdf
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+ - **Paper (Newer variants, extended benchmarks):** https://arxiv.org/pdf/2401.03955.pdf
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  ## Uses
 
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  **BibTeX:**
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  ```
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+ @misc{ekambaram2024tiny,
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+ title={Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series},
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+ author={Vijay Ekambaram and Arindam Jati and Pankaj Dayama and Sumanta Mukherjee and Nam H. Nguyen and Wesley M. Gifford and Chandra Reddy and Jayant Kalagnanam},
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+ year={2024},
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+ eprint={2401.03955},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.LG}
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  }
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  ```
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  **APA:**
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+ Ekambaram, V., Jati, A., Dayama, P., Mukherjee, S., Nguyen, N. H., Gifford, W. M., Kalagnanam, J. (2024). Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. arXiv [Cs.LG]. Retrieved from http://arxiv.org/abs/2401.03955
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  ## Model Card Authors