DIAGNOSTIC CHECKS OF NONSTANDARD TIME-SERIES MODELS
DIAGNOSTIC CHECKS OF NONSTANDARD TIME-SERIES MODELS
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DOI：
10.1002/for.3980040305
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发表时间：
1985-07-01
影响因子：
3.4
通讯作者：
SMITH, JQ
中科院分区：
文献类型：
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作者：
SMITH, JQ
自Box和Jenkins(1970)的ARIMA建模技术流行以来，诊断检查已成为帮助评估预测系统充分性的标准工具。然而，大多数研究都对正常或二阶平稳模型进行了检验。本文给出了可以简单地对非正态、非标准模型进行的各种诊断检验，例如多过程模型(Harison and Stevens，1976)，其中残差肯定不是正态的。到目前为止，这些模型的性能可以在网上进行客观的审查。文中给出了算例，包括一种广义求和技术，以说明该技术在特定序列上的有效性。
Diagnostic checks have become a standard tool for helping to assess the adequacy of a forecasting system since Box and Jenkins' (1970) ARIMA modelling technique became popular. However, most of the research has developed checks for normal or second‐order stationary models. This paper gives various diagnostic checks that can be performed simply on nonnormal, non‐standard models such as the class of multiprocess models (Harrison and Stevens, 1976), where residuals are definitely not normal. The performance to date of these models can then be objectively scrutinized on‐line. Examples, including a generalized cusum technique, are given to illustrate the effectiveness of the techniques on specific series.