TimePre unifies MLP speed and MCL distributional power via Stabilized Instance Normalization to deliver SOTA probabilistic accuracy, orders-of-magnitude faster inference, and improved stability over prior MCL methods.
Deepar: Probabilistic forecasting with autoregressive recurrent net- works.International Journal of Forecasting, 36(3):1181– 1191, 2020
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TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
TimePre unifies MLP speed and MCL distributional power via Stabilized Instance Normalization to deliver SOTA probabilistic accuracy, orders-of-magnitude faster inference, and improved stability over prior MCL methods.