Temporal Trends Analysis for Dengue Outbreak and Network Threats Severity Prediction Accuracy Improvement
Nurfadhlina Mohd Sharef, Nor Azura Husin, Khairul Azhar Kasmiran, Mohd Izuan Ninggal
Journal of Digital Information Management, Vol. 17, No. 3, 2019.
DOI: 10.6025/jdim/2019/17/3/122-132
Link: http://dline.info/fpaper/jdim/v17i3/jdimv17i3_2.pdf
Abstract:
Time series analysis is one of the major
techniques in capturing trends and pattern of the occurrence
for future forecasting. Existing but scarce works
have developed temporal-based techniques which target
to either predict movement (increase or decrease) or quantify
the possibility of the predicted event to happen. Many
of these techniques focus on the values of the time series
attribute but there is no available work on dengue or
intrusion logs that focus on temporal trend analysis-based
on temporal relations mining. In this work the proposed
technique utilize the temporal trends analysis of the observational
attributes towards the pattern of the target's
attribute values. In this work, we propose a new temporal
trends analysis approach that uses temporal relation mining
in forecasting dengue outbreak and cyber intrusion.
We leverage the temporal abstractions and temporal logic
to define patterns with the goal to optimize prediction
accuracy. From the experiment conducted, the results
showed that the proposed approach has better prediction
as compared to the baseline.