Generative Probabilistic Time Series Forecasting and Applications in Grid Operations

The main challenge of applying Wiener-Kallianpur innovation Whereas standard probabilistic forecasting aims to estimate representation for inference and decision-making is the conditional probability distribution of the time series at twofold. First, obtaining a causal encoder to extract the a future time, GPF obtains a generative model capable of innovation process requires knowing the marginal and joint producing arbitrarily many Monte Carlo samples of future distributions of the time series, which is rarely possible without time series realizations according to the conditional probability imposing some parametric structure. Furthermore, even when distribution of the time series given past observations. As the probability distribution is known, there is no known computationally a nonparametric probabilistic forecasting technique, GPF is tractable way to construct the causal encoder to essential for decision-making under uncertainty where datadriven obtain an innovation process. Second, the Wiener-Kallianpur risk-sensitive optimization requires conditional samples innovation representation may not exist for a broad class of of future randomness. The Monte Carlo samples generated random processes, including some of the important cases of from GPF can be used to produce any form of point forecast.