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Some Recent Advances in Forecasting and Control

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  • G. E. P. Box
  • G. M. Jenkins

Abstract

A brief discussion of Statistical Quality Control Charting procedures is first presented with special reference to the relevance of the objectives and assumptions. An approach to the design of discrete feedforward and feedback control schemes, which are of great importance for example, in the chemical industry, is then given. This approach to control employs discrete stochastic and dynamic models discussed in Part I of this paper (Box and Jenkins, 1968) and has a close link with the forecasting problems discussed there. The control algorithms obtained are ideally suited to discrete digital computer control. However, for common simple situations the algorithms may be represented by suitable charts or nomograms which may be employed to obtain improved manual control. The paper ends with a discussion of a problem typical of that arising in the parts manufacturing industry. Here, attention must be given to the cost of making an adjustment to the machine as well as to the cost of being off target and to the stochastic nature of the disturbance. An example is given where the appropriate form of action is like that required by Roberts's modification of a Shewhart chart. However, the justification required to make such action appropriate is very different from that previously given.
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Suggested Citation

  • G. E. P. Box & G. M. Jenkins, 1968. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 17(2), pages 91-109, June.
  • Handle: RePEc:bla:jorssc:v:17:y:1968:i:2:p:91-109
    DOI: 10.2307/2985674
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    2. Fieger, Peter & Rice, John, 2016. "Modelling Chinese Inbound Tourism Arrivals into Christchurch," MPRA Paper 75468, University Library of Munich, Germany.
    3. Xia Pan & Jeffrey Jarrett, 2012. "Why and how to use vector autoregressive models for quality control: the guideline and procedures," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(3), pages 935-948, April.
    4. Ralf Barkemeyer & Philippe Givry & Frank Figge, 2018. "Trends and patterns in sustainability-related media coverage: A classification of issue-level attention," Environment and Planning C, , vol. 36(5), pages 937-962, August.
    5. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    6. Amirhossein Sohrabbeig & Omid Ardakanian & Petr Musilek, 2023. "Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess," Forecasting, MDPI, vol. 5(4), pages 1-13, December.
    7. Salvatore Carta & Andrea Medda & Alessio Pili & Diego Reforgiato Recupero & Roberto Saia, 2018. "Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data," Future Internet, MDPI, vol. 11(1), pages 1-19, December.
    8. Christian Sonesson, 2003. "Evaluations of some Exponentially Weighted Moving Average methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1115-1133.
    9. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    10. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    11. Dat Thanh Tran & Alexandros Iosifidis & Juho Kanniainen & Moncef Gabbouj, 2017. "Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis," Papers 1712.00975, arXiv.org.
    12. Phinikarides, Alexander & Kindyni, Nitsa & Makrides, George & Georghiou, George E., 2014. "Review of photovoltaic degradation rate methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 143-152.
    13. Xing Wang & Yijun Wang & Bin Weng & Aleksandr Vinel, 2020. "Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network," Papers 2010.01197, arXiv.org.
    14. Zhao, Jiandong & Yu, Zhixin & Yang, Xin & Gao, Ziyou & Liu, Wenhui, 2022. "Short term traffic flow prediction of expressway service area based on STL-OMS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    15. Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
    16. Alexander Frick & George Makrides & Markus Schubert & Matthias Schlecht & George E. Georghiou, 2020. "Degradation Rate Location Dependency of Photovoltaic Systems," Energies, MDPI, vol. 13(24), pages 1-20, December.
    17. Phinikarides, Alexander & Makrides, George & Zinsser, Bastian & Schubert, Markus & Georghiou, George E., 2015. "Analysis of photovoltaic system performance time series: Seasonality and performance loss," Renewable Energy, Elsevier, vol. 77(C), pages 51-63.
    18. Shivshanker Patel & Parthasarathy Ramachandran, 2015. "A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 589-602, January.

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