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Quadratic Direct Forecast for Training Multi-Step Time-Series Forecast Models

arXiv — stat.ML•Tuesday, November 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
A new study on arXiv introduces a quadratic direct forecast method for training multi-step time-series forecasting models. This approach addresses key issues in existing training objectives, such as the mean squared error, which often treats future steps as independent tasks. By considering label autocorrelation and setting different weights for various forecasting tasks, this method promises to enhance the accuracy and reliability of predictions. This advancement is significant for industries relying on precise forecasting, as it could lead to better decision-making and resource allocation.
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