{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:30:09Z","timestamp":1760239809126,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T00:00:00Z","timestamp":1546473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>In this paper, auto-regressive integrated moving average (ARIMA) time-series data forecast models are evaluated to ascertain their feasibility in predicting human\u2013machine interface (HMI) state transitions, which are modeled as multivariate time-series patterns. Human\u2013machine 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.<\/jats:p>","DOI":"10.3390\/make1010018","type":"journal-article","created":{"date-parts":[[2019,1,3]],"date-time":"2019-01-03T11:11:56Z","timestamp":1546513916000},"page":"287-311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Evaluation of ARIMA Models for Human\u2013Machine Interface State Sequence Prediction"],"prefix":"10.3390","volume":"1","author":[{"given":"Harsh V. P.","family":"Singh","sequence":"first","affiliation":[{"name":"Computers, Controls and Design Department, Ontario Power Generation, Pickering, ON L1V 2R5, Canada"},{"name":"Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0472-5757","authenticated-orcid":false,"given":"Qusay H.","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,3]]},"reference":[{"key":"ref_1","unstructured":"International Atomic Energy Agency (IAEA) (2018, August 02). International Nuclear Event Scale (INES). Available online: http:\/\/www-ns.iaea.org\/tech-areas\/emergency\/ines.asp."},{"key":"ref_2","unstructured":"IAEA (2018, August 02). INSAG-7, The Chernobyl Accident, Updating of INSAG-1. Available online: http:\/\/www-pub.iaea.org."},{"key":"ref_3","unstructured":"Nuclear Energy Institute (2018, August 02). Lessons from the 1979 Accident at Three Mile Island. Available online: http:\/\/www.nei.org."},{"key":"ref_4","unstructured":"Atomic Energy of Canada Limited, Chalk River Laboratories (2018, August 02). The Chalk River Accident in 1952\u2014The Canadian Nuclear FAQ. Available online: http:\/\/www.nuclearfaq.ca."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1177\/1555343411433844","article-title":"Human performance consequences of automated decision aids: The impact of degree of automation and system experience","volume":"6","author":"Manzey","year":"2012","journal-title":"J. Cogn. Eng. Decis. Mak."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Singh, H.V., and Mahmoud, Q.H. (May, January 30). Eye-on-HMI: A framework for monitoring human machine interfaces in control rooms. Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada.","DOI":"10.1109\/CCECE.2017.7946695"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Singh, H.V., and Mahmoud, Q.H. (2017, January 16\u201318). ViDAQ: A framework for monitoring human machine interfaces. Proceedings of the 2017 IEEE 20th International Symposium on Real-Time Distributed Computing (ISORC), Toronto, ON, Canada.","DOI":"10.1109\/ISORC.2017.25"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Swain, A.D., and Guttmann, H.E. (1983). Handbook of Human-Reliability Analysis with Emphasis on Nuclear Power Plant Applications.","DOI":"10.2172\/5752058"},{"key":"ref_9","unstructured":"Ye, H., and Zheng, W. (2016, January 23\u201325). A human reliability analysis method based on cognitive process model for risk assessment. Proceedings of the 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Birmingham, UK."},{"key":"ref_10","first-page":"17","article-title":"Cream\u2014A second generation human reliability analysis method","volume":"10","author":"Yao","year":"2005","journal-title":"Ind. Eng. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mu, L., Xiao, B., Xue, W., and Yuan, Z. (2015, January 6\u20139). The prediction of human error probability based on bayesian networks in the process of task. Proceedings of the 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore.","DOI":"10.1109\/IEEM.2015.7385625"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/S0169-8141(97)00079-6","article-title":"Effects of performance shaping factors on human error","volume":"22","author":"Mackieh","year":"1998","journal-title":"Int. J. Ind. Ergon."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1177\/154193120805202109","article-title":"Human error quantification using performance shaping factors in the spar-h method","volume":"Volume 52","author":"Blackman","year":"2008","journal-title":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lee, H.-C., Jang, T.-I., and Moon, K. (2017, January 11\u201314). Anticipating human errors from periodic big survey data in nuclear power plants. Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA.","DOI":"10.1109\/BigData.2017.8258539"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.ress.2006.05.014","article-title":"Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents: Part 1: Overview of the idac model","volume":"92","author":"Chang","year":"2007","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5999","DOI":"10.1109\/TIE.2017.2782236","article-title":"Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models","volume":"65","author":"Xie","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.eng.2018.07.015","article-title":"A hardware platform framework for an intelligent vehicle based on a driving brain","volume":"4","author":"Li","year":"2018","journal-title":"Engineering"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wardah, T., Kamil, A., Hamid, A.S., and Maisarah, W. (2011, January 5\u20136). Statistical verification of numerical weather prediction models for quantitative precipitation forecast. Proceedings of the 2011 IEEE Colloquium on Humanities, Science and Engineering (CHUSER), Penang, Malaysia.","DOI":"10.1109\/CHUSER.2011.6163865"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gao, T., Chai, Y., and Liu, Y. (2017, January 24\u201326). Applying long short term memory neural networks for predicting stock closing price. Proceedings of the 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS.2017.8342981"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, C., Xu, Q., and Liu, Y. (2010, January 22\u201323). A new model between stock valuation index and volatility of stock price. Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics (ICICCI), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICICCI.2010.122"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4224","DOI":"10.1109\/TII.2018.2822828","article-title":"Object classification using cnn-based fusion of vision and lidar in autonomous vehicle environment","volume":"14","author":"Gao","year":"2018","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_22","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control, John Wiley & Sons. [5th ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wijaya, Y.B., Kom, S., and Napitupulu, T.A. (2010, January 2\u20133). Stock price prediction: Comparison of ARIMA and artificial neural network methods-an indonesia stock\u2019s case. Proceedings of the 2010 Second International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT), Jakarta, Indonesia.","DOI":"10.1109\/ACT.2010.45"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3606","DOI":"10.1016\/j.apenergy.2010.05.012","article-title":"Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models","volume":"87","author":"Tan","year":"2010","journal-title":"Appl. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bi, J., Zhang, L., Yuan, H., and Zhou, M. (2018, January 27\u201329). Hybrid task prediction based on wavelet decomposition and ARIMA model in cloud data center. Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China.","DOI":"10.1109\/ICNSC.2018.8361342"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Janardhanan, D., and Barrett, E. (2017, January 11\u201314). CPU workload forecasting of machines in data centers using LSTM recurrent neural networks and ARIMA models. Proceedings of the 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), Cambridge, UK.","DOI":"10.23919\/ICITST.2017.8356346"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rath, A., Samantaray, S., Bhoi, K.S., and Swain, P.C. (2017, January 1\u20132). Flow forecasting of hirakud reservoir with ARIMA model. Proceedings of the 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), Chennai, India.","DOI":"10.1109\/ICECDS.2017.8389997"},{"key":"ref_28","first-page":"277","article-title":"Lag order and critical values of the augmented dickey\u2013fuller test","volume":"13","author":"Cheung","year":"1995","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yang, D., Chen, H., Song, Y., and Gong, Z. (2017, January 9\u201310). Granger causality for multivariate time series classification. Proceedings of the 2017 IEEE International Conference on Big Knowledge (ICBK), Hefei, China.","DOI":"10.1109\/ICBK.2017.36"},{"key":"ref_30","unstructured":"Singh, H.V., and Mahmoud, Q.H. (2018). Non-Intrusive Monitoring of Operator Situational Awareness Via Human-Machine Interface States. 8th International Conference on Simulation Methods in Nuclear Science and Engineering, Canadian Nuclear Society (CNS-SNC)."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/18\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:23:25Z","timestamp":1760185405000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/1\/1\/18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,3]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["make1010018"],"URL":"https:\/\/doi.org\/10.3390\/make1010018","relation":{},"ISSN":["2504-4990"],"issn-type":[{"type":"electronic","value":"2504-4990"}],"subject":[],"published":{"date-parts":[[2019,1,3]]}}}