Cross validation for uncertain autoregressive model

Uncertain time series models have been investigated to predict future values based on imprecise observations. The existing researches focus on how to estimate unknown parameters in the uncertain time series model without considering how to determine the lag order. This paper proposes three types of...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 51; no. 8; pp. 4715 - 4726
Main Authors Liu, Zhe, Yang, Xiangfeng
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 03.08.2022
Taylor & Francis Ltd
Subjects
Autoregressive models
Average testing error
Cross validation
Imprecise observations
Lag order
Parameter uncertainty
Time series
Uncertain time series
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Summary:Uncertain time series models have been investigated to predict future values based on imprecise observations. The existing researches focus on how to estimate unknown parameters in the uncertain time series model without considering how to determine the lag order. This paper proposes three types of cross validation methods, i.e. fixed origin cross validation, rolling origin cross validation, and rolling window cross validation to choose the lag order considering the model's prediction ability, and derives corresponding calculation methods under the framework of uncertainty theory. A numerical example and a real data example illustrate our methods in detail.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2020.1747077