Generative Probabilistic Time Series Forecasting and Applications in Grid Operations
2024 58th Annual Conference on Information Sciences and Systems (CISS)(2024)
摘要
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.
更多查看译文
关键词
Probabilistic time series forecasting,innovation representation,autoencoder,generative adversarial networks
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要