Analysis of Uncertainty in Time Series Data: Issues and Challenges

This paper reviews issues and challenges of uncertainty in time series data. The aim of uncertainty analysis is to determine the ways of how to deal with uncertain data in order to gain knowledge, fit low dimensional model, and do prediction. So as to build an efficient predictive tool, uncertainty in data could not be ruled out because it may bring important knowledge. Uncertainty information arises from different resources such as process uncertainty, model uncertainty or data uncertainty. In this paper, issues and challenges of these uncertainties in time series data will be discovered and how these issues could be solved by data mining techniques will be discussed. Frequent pattern mining algorithm through FP-growth, Apriori algorithm and H-mine are methods that could be used to investigate the existing of uncertainty data. Meanwhile, Euclidean distance, particle swarm optimization, Monte Carlo simulation, and regression are methods that could be compared as prediction methods. These methods have been implemented in many data types since early 1900s. Also, this paper shows results of the uncertainty detection test on time series data sets. The test aims to prove the existing of uncertainty in the data. This work will benefit in many application domains.

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