Probabilistic forecasting of symbol sequences with deep neural networks

Michael Hauser, Yiwei Fu, Yue Li, Shashi Phoha, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Time series forecasting is usually done in a deterministic sense, such as in autoregressive moving average models, where a future state is predicted as a linear combination of past events. However, by formulating the problem in a probabilistic sense, soft predictions are obtained from a given probability mass function. This paper uses a deep neural network for probabilistic forecasting of time series by minimizing the cross entropy of the probability of future symbols from a given state. The advantage of this type of model is that it makes probabilistic inferences from the ground up, and without any restrictive assumptions (e.g., second order statistics). The efficacy of the proposed model is tested by forecasting the emergence of combustion instabilities, defined to be the root mean square of the pressure signal inside a laboratory-scale combustor system. The proposed algorithm has been compared with the autoregressive moving average (ARMA) model, which acts as a baseline for many time-series forecasting tasks, and the proposed model is shown to significantly outperform the ARMA model in this task.

Original languageEnglish (US)
Title of host publication2017 American Control Conference, ACC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509059928
StatePublished - Jun 29 2017
Event2017 American Control Conference, ACC 2017 - Seattle, United States
Duration: May 24 2017May 26 2017

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2017 American Control Conference, ACC 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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