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Modelling Seasonally Varying Data: A Case Study for Sudden Infant Death Syndrome (SIDS)

机译:为季节性变化的数据建模:婴儿猝死综合症(SIDS)的案例研究

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Many time series are measured monthly, either as averages or totals, and such data often exhibit seasonal variability - the values of the series are consistently larger for some months of the year than for others. A typical series of this type is the number of deaths each month attributed to SIDS (Sudden Infant Death Syndrome). Seasonality can be modelled in a number of ways. This paper describes and discusses various methods for modelling seasonality in SIDS data, though much of the discussion is relevant to other seasonally varying data. There are two main approaches, either fitting a circular probability distribution to the data, or using regression-based techniques to model the mean seasonal behaviour. Both are discussed in this paper.
机译:每个月都会测量许多时间序列,无论是平均值还是总和,这些数据通常都表现出季节性变化性-在一年中的某些月份,该序列的值始终大于其他月份。这种类型的典型序列是归因于SIDS(婴儿猝死综合症)的每月死亡人数。季节性可以通过多种方式建模。本文描述并讨论了在SIDS数据中模拟季节性的各种方法,尽管其中许多讨论与其他季节性变化的数据有关。有两种主要方法,一种是将圆形概率分布拟合到数据,或者使用基于回归的技术来模拟平均季节性行为。两者都在本文中讨论。

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