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Soft Computing

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12-06-2020 | Methodologies and Application

Least absolute deviations estimation for uncertain autoregressive model

Authors: Xiangfeng Yang, Gyei-Kark Park, Yancai Hu

Published in: Soft Computing | Issue 23/2020

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Abstract

To predict future values based on imprecisely observed values, uncertain time series has been proposed, and the least-squares method has been presented to estimate the unknown parameters of uncertain autoregressive models. This paper considers the least absolute deviations estimation of uncertain autoregressive model, and a minimization problem is derived to calculate the unknown parameters in the uncertain autoregressive model. Finally, some numerical examples are given to illustrate the robustness of the least absolute deviations estimation compared with the least-squares estimation.

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Title: Least absolute deviations estimation for uncertain autoregressive model
Authors: Xiangfeng Yang
Gyei-Kark Park
Yancai Hu
Publication date: 12-06-2020
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 23/2020
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-05079-0

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