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Quality quandaries: Practical time series modeling
Isenberg School of Management, University of Massachusetts Amherst, Eugene M. Isenberg School of Management, University of Massachusetts Amherst.
Department of Industrial Engineering, Arizona State University, Tempe.ORCID iD: 0000-0003-4222-9631
2007 (English)In: Quality Engineering, ISSN 0898-2112, E-ISSN 1532-4222, Vol. 19, no 3, p. 253-262Article in journal (Refereed) Published
Abstract [en]

Time series analysis is important in modern quality monitoring and control. The analysis has no precise methods and no single true, final answer. There are three general classes for stationary time series models: autoregressive (AR), moving average (MA) or the autoregressive moving average model. If in case a data is nonstationary, differencing before using ARMA model to fit to the data is necessary. The formulations for AR(p), MA(q) and the ARMA(p,q) has zero intercept which is attained by subtracting the average from the stationary data before modeling the process. If it is applied in a nonstationary data, there is a need to differenced either once or twice, adding a nonzero intercept term to the model. This implies that there is an underlying deterministic first or second order polynomial trend in the data. In reality, the type of model and the order necessary to adequately model a given process is not known. Hence, there is a need to determine the model that best fit the data based on looking at the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Since the time series modeling requires judgment and experience, an literative model is suggested. Once the model is fitted, diagnostic checks are conducted using the ACF and PACF. Series C consisting of 226 observations of the temperature of a chemical pilot plant has been used as an example.

Place, publisher, year, edition, pages
2007. Vol. 19, no 3, p. 253-262
National Category
Reliability and Maintenance
Research subject
Quality Technology and Management
Identifiers
URN: urn:nbn:se:ltu:diva-8266DOI: 10.1080/08982110701577837Local ID: 6beac75e-383a-4a86-a1c2-7fd072ef88d6OAI: oai:DiVA.org:ltu-8266DiVA, id: diva2:981157
Note
Upprättat; 2007; 20150602 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-03-16Bibliographically approved

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