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Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence Fudan University
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By randomly attending to different time patches and progressively mixing scales, SEMixer achieves state-of-the-art long-term time series forecasting with a lightweight architecture.
Stop letting noisy, low-predictability data ruin your time series models: APTF dynamically identifies and penalizes these samples during training, leading to improved forecasting and classification accuracy.
