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General Information
Editor-in-chief

Prof. Pascal Lorenz
University of Haute Alsace, France
 
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.

IJFCC 2016 Vol.5(6): 233-236 ISSN: 2010-3751
doi: 10.18178/ijfcc.2016.5.6.477

Approaches of Handling Uncertain Time Series Data towards Prediction

Radzuan M. F. Nabilah, Zalinda Othman, and Bakar A. Azuraliza
Abstract—This paper works on clustering issues of uncertain time series data prior to prediction process. The aim of uncertainty analysis is to determine how to deal with uncertain data in order to gain knowledge, fit low dimensional model, and to predict. So as to gain a reliable prediction, uncertainty in data could not be ruled out because it may bring important knowledge. Clustering as a step before prediction process can be seen as the most popular representative of unsupervised learning, while classification together with regression are possibly the most frequently considered tasks in supervised learning. Clustering uncertain time series data posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. This work will benefit in many application domains.

Index Terms—Clustering, prediction, time series data, uncertain time series data, uncertainty.

The authors are with the Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia (e-mail: nabilah.filzahr@gmail.com, zalinda@ukm.edu.my, azuraliza@ukm.edu.my).

[PDF]

Cite: Radzuan M. F. Nabilah, Zalinda Othman, and Bakar A. Azuraliza, "Approaches of Handling Uncertain Time Series Data towards Prediction," International Journal of Future Computer and Communication vol. 5, no. 6, pp. 233-236, 2016.

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