.png){kind=icon}
.png){kind=icon}
Return
Least absolute deviations estimation for uncertain autoregressive model
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-020-05079-0.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Confidence interval
Mean absolute error
LAD
Uncertain autoregressive
Uncertain variable
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W