Generative Probabilistic Time Series Forecasting and Applications in
  Grid Operations
Xinyi Wang Lang Tong Qing Zhao
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.