Generative Probabilistic Forecasting with Applications in Market Operations

arXiv:2403.05743v1 Announce Type: cross
Abstract: This paper presents a novel generative probabilistic forecasting approach derived from the Wiener-Kallianpur innovation representation of nonparametric time series. Under the paradigm of generative artificial intelligence, the proposed forecasting architecture includes an autoencoder that transforms nonparametric multivariate random processes into canonical innovation sequences, from which future time series samples are generated according to their probability distributions conditioned on past samples. A novel deep-learning algorithm is proposed that constrains the latent process to be an independent …

abstract applications architecture artificial artificial intelligence arxiv autoencoder canonical cs.lg econ.gn eess.sp forecasting future generative generative artificial intelligence innovation intelligence market multivariate novel operations paper paradigm processes q-fin.ec random representation series time series type