Imputation of Missing Values in Daily Wind Speed Data Using Hybrid AR-ANN Method

Osamah Basheer Shukur and Muhammad Hisyam Lee

Modern Applied Science, 2015, vol. 9, issue 11, 1

Abstract: Wind speed data collection process faces several problems as failure of data observing devices. Therefore, windspeed data naturally contains missing values. Imputing these missing values using an effective method isimportant before performing time series analysis. The classical methods as linear, nearest neighbor, and statespace may not provide accurate imputations when the wind speed contains nonlinearity. In this study, the hybridartificial neural network (ANN) and autoregressive (AR) method is proposed for imputing the missing values.ANN is a nonlinear method that is capable of imputing the missing values in wind speed data with nonlinearcharacteristic. AR model is used for determining the structure of the input layer for the ANN. Listwise deletion isused before AR modeling to handle the missing values. A case study is carried out using daily Iraqi andMalaysian wind speed data. The proposed imputation method is compared with linear, nearest neighbor, andstate space methods. The comparison has shown that AR-ANN outperformed the classical methods. Inconclusion, the missing values in wind speed data with nonlinear characteristic can be imputed more accuratelyusing AR-ANN. Therefore, imputing the missing values using AR-ANN leads to more accurate performance oftime series modeling and analysis.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/50119/26960 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/50119 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:9:y:2015:i:11:p:1

Access Statistics for this article

More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().