English to Chinese: Outlier Detection in Regression Models with ARIMA Errors using Robust Estimates（sample）
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Detailed field: IT (Information Technology) | |
Source text - English
It is well known that time series data are very often contaminated with outliers or affected by structural changes as level shifts. These disturbances can affect all the stages of time series analysis: model identification, estimation and forecasting.
The impact of outliers in the estimation of parameters of integrated autoregressive moving average (ARIMA) models has been studied, among others, by Denby and Martin (1979), Chang and Tiao (1983), Martin and Yohai (1986), Pe˜na (1987, 1990, 1991) and Bianco et al. (1996).
Outliers may influence forecasting in two different ways:
(1) The optimal predictor for an ARIMA model depends on its parameters. Therefore, the bias in parameter estimates produced by outliers will decrease its efficiency.
(2) The optimal predictor is a linear combination of the observed data, and generally, the largest coefficients are those corresponding to observations near to the forecast origin. Therefore the presence of outliers among these observations may have a large impact on forecasts. However, the prediction error will depend on the type of outliers: an additive outlier may increase the predictor error considerably, but an innovation outlier will not have any effect. The influence of different types of outliers in forecasting can be found in Chen and Liu (1993a) and Ledolter (1988, 1991). | Translation - Chinese
众所周知，时间序列的数据通常会受到异常值或电平位移等结构改变的不良影响。这些扰值会影响时间序列的每一级分析，包括：模型确立，模型评估以及模型预测。
异常值在差分自回归滑动平均模型（ARIMA模型）中的影响在过去已有相关研究，其中包括Denby和Martin(1979)， Chang和Tiao(1983)，Martin和Yohai(1986)，Pena(1987，1990，1991)和Bianco等(1996)。
异常值对模型预测的影响形式有两种：
(1) ARIMA模型的最优预测值取决于它的参数，所以由异常值造成的参数评估偏差将会降低其效率。
(2) 最优预测值是对测得数据的一个线性组合，而且效率最大值通常会出在预测初始值附近，所以观测数据中异常值的存在将会对预测造成巨大的影响。然而，预测误差将取决于异常值的类别，例如：加性异常值可能会极大地升高预测误差，但新息异常值则不会造成任何影响。异常值的类别对预测的影响可以在Chen和Liu(1993a)以及Ledolter(1988,1991)的研究中找到。
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