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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| Published in | Communications in statistics. Simulation and computation Vol. 51; no. 8; pp. 4715 - 4726 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Philadelphia
Taylor & Francis
03.08.2022
Taylor & Francis Ltd |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Liu, Zhe Yang, Xiangfeng |
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| SubjectTerms | Autoregressive models Average testing error Cross validation Imprecise observations Lag order Parameter uncertainty Time series Uncertain time series |
| Title | Cross validation for uncertain autoregressive model |
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