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README.md
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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
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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.
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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.
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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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