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--- |
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library_name: transformers |
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base_model: |
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- amazon/chronos-bolt-small |
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pipeline_tag: time-series-forecasting |
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--- |
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# Model Card for Chronos Bolt Small Fine-Tuned Model |
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<img align="center" height="350" src="https://media3.giphy.com/media/v1.Y2lkPTc5MGI3NjExYjRmcWUwaGFkbW1lczJoYzBjbHBxZjMyeDdhdDQycGdzamwyOGhiZyZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9Zw/ZtB2l3jHiJsFa/giphy.gif"/> </p> |
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## Summary |
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This model is fine-tuned for time-series forecasting tasks and serves as a tool for both practical predictions and research into time-series demand forecasting. It is based on the `amazon/chronos-bolt-small` model and has been adapted using a dataset with 25 million rows of proprietary time-series data. Due to confidentiality restrictions, dataset details cannot be shared. |
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## Fine-Tuning Dataset |
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The model was fine-tuned on a proprietary dataset containing 25 million rows of time-series data. While details about the dataset are confidential, the following general characteristics are provided: |
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- The dataset consists of multi-dimensional time-series data. |
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This large-scale dataset ensures the model captures complex patterns and temporal dependencies necessary for accurate forecasting. |
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#### Summary |
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The fine-tuned model performs well on intermitent demand forecasting. |
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## Technical Specifications |
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### Model Architecture and Objective |
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The model is based on the `amazon/chronos-bolt-small` architecture, fine-tuned specifically for time-series forecasting tasks. It leverages pre-trained capabilities for sequence-to-sequence modeling, adapted to handle multi-horizon forecasting scenarios. |
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## Contact: |
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[NIEXCHE (Fevzi KILAS)](https://niexche.github.io/) |
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