用一种新方法让时间序列模型一次前向传播就生成有真实相关性的预测路径。
Efficiently Generating Correlated Sample Paths from Multi-step Time Series Foundation Models
- 基于拷贝函数，一次前向传播生成带相关性的多步预测路径
- 速度比传统自回归采样快多个数量级，且误差更小
- 适合需要高效高质样本路径的金融、气象等场景
许多时间序列应用需要以样本路径形式获取多步预测轨迹。近期的时间序列基础模型通过多步前瞻预测提升了多步预测的质量与效率，但这些模型仅预测各时间步的独立边缘分布，而非完整的联合预测分布。为生成具有真实相关结构的预测路径，通常需采用自回归采样，但计算成本极高。本文提出一种基于拷贝函数的方法，可从现有的多步时间序列基础模型中高效生成准确的、具相关性的样本路径，仅需一次前向传播。该方法生成相关路径的速度比自回归采样快多个数量级，并通过缓解误差累积现象提升了样本路径质量。
原文摘要 · Abstract (English)
Many time series applications require access to multi-step forecast trajectories in the form of sample paths. Recently, time series foundation models have leveraged multi-step lookahead predictions to improve the quality and efficiency of multi-step forecasts. However, these models only predict independent marginal distributions for each time step, rather than a full joint predictive distribution. To generate forecast sample paths with realistic correlation structures, one typically resorts to autoregressive sampling, which can be extremely expensive. In this paper, we present a copula-based approach to efficiently generate accurate, correlated sample paths from existing multi-step time series foundation models in one forward pass. Our copula-based approach generates correlated sample paths orders of magnitude faster than autoregressive sampling, and it yields improved sample path quality by mitigating the snowballing error phenomenon.
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