B0775
Title: Deep distributional time series models and the probabilistic forecasting of intraday electricity prices
Authors: Nadja Klein - Karlsruhe Institute of Technology (Germany) [presenting]
Michael Stanley Smith - Melbourne Business School (Australia)
David Nott - National University of Singapore (Singapore)
Abstract: Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. Two approaches to constructing deep time series probabilistic models are proposed based on a variant of RNN called an echo state network (ESN). The first is where the output layer of the ESN has stochastic disturbances and a Bayesian prior for regularization. The second employs the implicit copula of an ESN with Gaussian disturbances, which is a Gaussian copula process on the feature space. Combining this copula process with a nonparametrically estimated marginal distribution produces a distributional time series model. The resulting probabilistic forecasts are deep functions of the feature vector and are marginally calibrated. In both approaches, Markov chain Monte Carlo methods are used to estimate the models and compute forecasts. The proposed models are suitable for the complex task of forecasting intraday electricity prices. Using data from the Australian market shows that the deep time series models provide accurate short-term probabilistic price forecasts, with the copula model dominating. Moreover, the models provide a flexible framework for incorporating probabilistic forecasts of electricity demand, which significantly increases upper tail forecast accuracy from the copula model.
