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).
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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.
