Generative probabilistic forecasting produces future time series samples according to the conditional probability distribution given past time series observations. Such techniques are essential in risk-based decision-making and planning under uncertainty with broad applications in grid operations, including electricity price forecasting, risk-based economic dispatch, and stochastic optimizations. Inspired by Wiener and Kallianpur's innovation representation, we propose a weak innovation autoencoder architecture and a learning algorithm to extract independent and identically distributed innovation sequences from nonparametric stationary time series. We show that the weak innovation sequence is Bayesian sufficient, which makes the proposed weak innovation autoencoder a canonical architecture for generative probabilistic forecasting. The proposed technique is applied to forecasting highly volatile real-time electricity prices, demonstrating superior performance across multiple forecasting measures over leading probabilistic and point forecasting techniques.

翻译：生成式概率预测根据过去时间序列观测值的条件概率分布，生成未来时间序列的样本。这类技术对于基于不确定性的风险决策与规划至关重要，在电网运行领域具有广泛应用，包括电价预测、风险经济调度及随机优化等。受Wiener和Kallianpur创新表示理论的启发，我们提出了一种弱创新自编码器架构及其学习算法，用于从非参数平稳时间序列中提取独立同分布的创新序列。我们证明了弱创新序列具有贝叶斯充分性，这使得所提出的弱创新自编码器成为生成式概率预测的规范架构。将该技术应用于高度波动的实时电价预测，结果表明，在多项预测指标上，该方法均优于主流的概率预测与点预测技术，展现出卓越性能。