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Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction

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Computers, Controls and Design Department, Ontario Power Generation, Pickering, ON L1V 2R5, Canada
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Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada
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Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(1), 287-311; https://doi.org/10.3390/make1010018
Received: 16 November 2018 / Revised: 22 December 2018 / Accepted: 24 December 2018 / Published: 3 January 2019
(This article belongs to the Section Learning)
In this paper, auto-regressive integrated moving average (ARIMA) time-series data forecast models are evaluated to ascertain their feasibility in predicting human–machine interface (HMI) state transitions, which are modeled as multivariate time-series patterns. Human–machine interface states generally include changes in their visually displayed information brought about due to both process parameter changes and user actions. This approach has wide applications in industrial controls, such as nuclear power plant control rooms and transportation industry, such as aircraft cockpits, etc., to develop non-intrusive real-time monitoring solutions for human operator situational awareness and potentially predicting human-in-the-loop error trend precursors. View Full-Text
Keywords: auto-regressive integrated moving average (ARIMA); human factor engineering (HFE); human–machine interface (HMI); human-in-the-loop (HITL); situational awareness (SA) auto-regressive integrated moving average (ARIMA); human factor engineering (HFE); human–machine interface (HMI); human-in-the-loop (HITL); situational awareness (SA)
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Singh, H.V.P.; Mahmoud, Q.H. Evaluation of ARIMA Models for Human–Machine Interface State Sequence Prediction. Mach. Learn. Knowl. Extr. 2019, 1, 287-311.

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