OUTLIERS, LEVEL SHIFTS, AND VARIANCE CHANGES IN TIME-SERIES
OUTLIERS, LEVEL SHIFTS, AND VARIANCE CHANGES IN TIME-SERIES
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DOI：
10.1002/for.3980070102
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发表时间：
1988-01-01
影响因子：
3.4
通讯作者：
TSAY, RS
中科院分区：
文献类型：
--
作者：
TSAY, RS
异常值、水平移动和方差变化在应用时间序列分析中很常见。然而，由于缺乏简单有效的方法来检测和处理这些异常事件，它们的存在和影响常常被忽视。考虑检测单变量时间序列中的异常值、水平移动和方差变化的问题。所采用的方法非常简单但很有用。仅使用最小二乘技术和残差方差比。通过分析三个真实数据集证明了这些简单方法的有效性。
Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.