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@@ -8,11 +8,11 @@ license: cdla-permissive-2.0
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  <img src="ttm_image.webp" width="600">
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  </p>
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- TTM, also known as TinyTimeMixer, are compact pre-trained models for Time-Series Forecasting, open-sourced by IBM Research.
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  **With less than 1 Million parameters, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.**
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  TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTM is pre-trained on diverse public time-series datasets which
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- can be easily fine-tuned for your target data. 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.)**
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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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  ### 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
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  - [Getting Started Notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb)
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  - [512-96 Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_512_96.ipynb)
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  - [1024-96 Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
 
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  ## Training Data
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  <img src="ttm_image.webp" width="600">
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  </p>
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+ TTM, also known as TinyTimeMixer, are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research.
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  **With less than 1 Million parameters, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.**
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  TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTM is pre-trained on diverse public time-series datasets which
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+ can be easily fine-tuned on your multi-variate target data. Refer to our [paper](https://arxiv.org/pdf/2401.03955v4.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.)**
 
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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/2401.03955v4.pdf).
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  TTM-1 currently supports 2 modes:
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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/2401.03955v4.pdf
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  ## Uses
 
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  - [Getting Started Notebook](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/ttm_getting_started.ipynb)
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  - [512-96 Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_512_96.ipynb)
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  - [1024-96 Benchmarks](https://github.com/IBM/tsfm/blob/main/notebooks/hfdemo/tinytimemixer/ttm_benchmarking_1024_96.ipynb)
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+ - Script for Finetuning with cross-channel correlation support - to be added soon
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  ## Training Data
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