用场景+概率对替代采样，提升时间序列预测的效率与准确性。
From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
- 直接输出有限个场景及其概率，避免传统采样方式的近似误差。
- TimePrism模型在5个数据集上9项指标达顶尖水平，仅1项落后。
- 适合追求高效、高精度预测的工业应用与研究者参考。
当前主流的概率时间序列预测模型依赖采样来表示未来不确定性，但存在概率不显式、覆盖不足和计算成本高等固有缺陷。本文提出「概率场景」新范式，通过直接生成有限个{场景, 概率}对，规避蒙特卡洛类近似。为验证该范式，我们设计了仅由三个并行线性层组成的TimePrism模型。令人意外的是，TimePrism在五个基准数据集上两个评估指标中取得9项领先结果。其有效性源于对学习目标的根本重构：不再建模连续概率空间，而是学习一组合理场景及其对应概率。本工作展示了概率场景范式的潜力，开辟了超越采样的预测新方向。
原文摘要 · Abstract (English)
Most state-of-the-art probabilistic time series forecasting models rely on sampling to represent future uncertainty. However, this paradigm suffers from inherent limitations, such as lacking explicit probabilities, inadequate coverage, and high computational costs. In this work, we introduce \textbf{Probabilistic Scenarios}, an alternative paradigm designed to address the limitations of sampling. It operates by directly producing a finite set of \{Scenario, Probability\} pairs, thus avoiding Monte Carlo-like approximation. To validate this paradigm, we propose \textbf{TimePrism}, a simple model composed of only three parallel linear layers. Surprisingly, TimePrism achieves 9 out of 10 state-of-the-art results across five benchmark datasets on two metrics. The effectiveness of our paradigm comes from a fundamental reframing of the learning objective. Instead of modeling an entire continuous probability space, the model learns to represent a set of plausible scenarios and corresponding probabilities. Our work demonstrates the potential of the Probabilistic Scenarios paradigm, opening a promising research direction in forecasting beyond sampling.
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