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| tags: |
| - model_hub_mixin |
| - pytorch_model_hub_mixin |
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| # CleanTS Model Card |
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| ## Introduction |
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| CleanTS is a competitive pre-trained baseline for time-series forecasting. It is designed to demonstrate the potential of **data-centric optimization** using a **standard encoder-only Transformer** architecture without any structural modifications. |
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| ## Key Technical Principles |
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| ### 1. Pure Architecture |
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| Zero modifications to the attention mechanism or model structure. CleanTS proves that high-quality data governance can significantly elevate the performance of vanilla architectures. |
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| ### 2. Systematic Data Governance |
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| CleanTS adheres to a strict data-centric pipeline: |
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| - **Zero Synthetic Data:** All training is performed exclusively on **authentic, real-world data**. |
| - **Publicly Sourced:** The training corpus consists entirely of **publicly available datasets**, ensuring transparency and accessibility. |
| - **Advanced Cleaning:** We achieve promising results solely through systematic data cleaning and preprocessing strategies. |
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| ### 3. Contamination Prevention |
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| We strictly guarantee that **no data from the GiftEval test set，Fev-bench，Fev-leaderboard and LSTF** was involved in the training phase. |
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| > [!NOTE] A **detailed technical report**, including our specific data cleaning methodologies, training configurations, and comprehensive ablation studies, will be released concurrently with the upcoming publication of the full model. |
