TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting
Published in Transactions on Machine Learning Research (TMLR), accepted, 2026
Probabilistic time-series forecasting methods tend to buy accuracy with heavy inference cost, or buy speed with unstable training. TimePre targets all three properties at once and evaluates the trade-off directly on long-horizon benchmarks.
Recommended citation: Jiang, L., Xu, L., Li, P., Hou, D., Ge, Q., Zhuang, D., Xing, S., Chen, W., Gao, X., et al. (2026). TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting. Transactions on Machine Learning Research.
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