{"title":"Time-series analysis of industrial accident data","authors":"Andris Freivalds, Alison B. Johnson","doi":"10.1016/0376-6349(90)90020-V","DOIUrl":null,"url":null,"abstract":"<div><p>Freivalds, A. and Johnson, A.B., 1990. Time-series analysis of industrial accident data. <em>Journal of Occupational Accidents</em>, 13: 179–193.</p><p>Considering the cyclical nature of accident and injury data, it is reasonable to consider the use of time-series analysis for modeling these data. One approach involved fitting a Box-Jenkins, auto-regressive, moving-average model to the data and using the model to forecast future values. A second approach utilized sine or cosine models to fit the cyclical pattern. A comparison of the two models, for a set of injury data in a glass manufacturing facility, indicated a clear superiority of the Box-Jenkins approach; not only for fitting a seasonal cycle, but also for accommodating monthly trends.</p></div>","PeriodicalId":100816,"journal":{"name":"Journal of Occupational Accidents","volume":"13 3","pages":"Pages 179-193"},"PeriodicalIF":0.0000,"publicationDate":"1990-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0376-6349(90)90020-V","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Occupational Accidents","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/037663499090020V","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Freivalds, A. and Johnson, A.B., 1990. Time-series analysis of industrial accident data. Journal of Occupational Accidents, 13: 179–193.
Considering the cyclical nature of accident and injury data, it is reasonable to consider the use of time-series analysis for modeling these data. One approach involved fitting a Box-Jenkins, auto-regressive, moving-average model to the data and using the model to forecast future values. A second approach utilized sine or cosine models to fit the cyclical pattern. A comparison of the two models, for a set of injury data in a glass manufacturing facility, indicated a clear superiority of the Box-Jenkins approach; not only for fitting a seasonal cycle, but also for accommodating monthly trends.
Freivalds, A.和Johnson, A. b .， 1990。工业事故数据的时序分析。职业事故学报，13(3):179-193。考虑到事故和伤害数据的周期性，考虑使用时间序列分析对这些数据建模是合理的。其中一种方法是将Box-Jenkins自动回归移动平均模型拟合到数据中，并用该模型预测未来的价值。第二种方法是利用正弦或余弦模型来拟合周期性模式。对一组玻璃制造工厂的损伤数据进行了两种模型的比较，表明Box-Jenkins方法具有明显的优势;不仅为了适应季节周期，也为了适应每月的趋势。
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