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.
Time series forecasting with deep learning: a survey.Philosophical Transactions of the Royal Society A, 379(2194):20200209, 2021
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.