Learning with Calibration: Exploring Test-Time Computing of Spatio-Temporal Forecasting
NeutralArtificial Intelligence
A recent study on spatio-temporal forecasting highlights the challenges faced in real-world applications, such as signal anomalies and noise. While existing solutions focus on enhancing robustness through complex network architectures, they often require significant computational resources. This research is important as it seeks to improve forecasting methods, which are vital in sectors like transportation and energy, ultimately aiming for more efficient and reliable predictions.
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