doi: 10.18178/ijfcc.2016.5.6.477
Approaches of Handling Uncertain Time Series Data towards Prediction
Radzuan M. F. Nabilah
,
Zalinda Othman
,
Bakar A. Azuraliza
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia.
Abstract
This paper works on clustering issues of uncertaintime series data prior to prediction process. The aim ofuncertainty analysis is to determine how to deal with uncertaindata in order to gain knowledge, fit low dimensional model, andto predict. So as to gain a reliable prediction, uncertainty in datacould not be ruled out because it may bring importantknowledge. Clustering as a step before prediction process can beseen as the most popular representative of unsupervisedlearning, while classification together with regression arepossibly the most frequently considered tasks in supervisedlearning. Clustering uncertain time series data posts significantchallenges on both modeling similarity between uncertainobjects and developing efficient computational methods. Thiswork will benefit in many application domains.
Keywords
- Clustering
- prediction
- time series data
- uncertain time series data
- uncertainty
How to 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. https://doi.org/ 10.18178/ijfcc.2016.5.6.477
Copyright & License
Copyright © 2016 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).