Deep probabilistic time series forecasting using augmented recurrent input for dynamic systems
Volume: 177, Pages: 109212 - 109212
Published: Sep 1, 2022
Abstract
The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specifically, we follow the popular encoder-decoder generative structure to build...
Paper Details
Title
Deep probabilistic time series forecasting using augmented recurrent input for dynamic systems
Published Date
Sep 1, 2022
Volume
177
Pages
109212 - 109212
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