Forecasting compositional time series
Forecasting compositional time series
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DOI：
10.1007/s11135-009-9229-8
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发表时间：
2010-06
影响因子：
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通讯作者：
T. Mills
中科院分区：
文献类型：
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作者：
T. Mills
组成数据集出现在许多学科中，并引起了一些有趣的统计考虑。近年来，成分时间序列的建模和预测取得了一些重要进展，尽管这种方法似乎并不广为人知。这份文件是纠正这种情况的一个小小的步骤。在简要介绍了成分数据集的基本结构，并概述了预测成分时间序列的影响，它说明了使用三个例子的技术：建模和预测支出份额在英国。经济;预测英国肥胖的趋势;并检查在一年中特定季度出生的英国一流板球运动员的比例变化。
Compositional data sets occur in many disciplines and give rise to some interesting statistical considerations. In recent years, the modelling and forecasting of compositional time series has seen some important developments, although this approach does not seem to be widely known. This paper represents a modest step towards rectifying this. After briefly setting out the basic structure of compositional data sets and outlining the implications for forecasting compositional time series, it illustrates the techniques using three examples: modelling and forecasting expenditure shares in the U.K. economy; forecasting trends in obesity in England; and examining shifts in the proportions of English first class cricketers born during particular quarters of the year.