Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

parameter for different classes of fluctuations and it can consistently outperform the
common prediction methods, such as the NWS predictor, AR(16), and ES [33] in a
grid environment. However, to the best of our knowledge, no existing work has been
proposed so far to effectively address the forecasting of activity durations in
scientific cloud workflow systems.
Time-series patterns are those time-series segments which have high probability
to repeat themselves (similar in the overall shape but maybe with different
amplitudes and/or durations) in a large time-series dataset [13, 39, 45, 57]. Timeseries
patterns can be employed for complex prediction tasks, such as trend analysis
and value estimation in stock market, product sales and weather forecast [43]. The
two major tasks in pattern based time-series forecasting are pattern recognition
which discovers time-series patterns according to the pattern definition, and pattern
matching which searches for the similar patterns with given query sequences. To
facilitate complex time-series analysis such as time-series clustering and fast
similarity search, time-series segmentation is often employed to reduce the variances
in each segment so that they can be described by simple linear or non-linear models
[48]. In general, most segmentation algorithms can be classified into three generic
algorithms with some variations which are Sliding Windows, Top-Down and
Bottom-Up [48]. The process of Sliding Window is like sequential scanning where
the segments keep increasing with each new point until some thresholds are violated.
The basic idea of the Top-Down algorithm is the repetition of the splitting process
where the original time-series is equally divided until all segments meet the stopping
criteria. In contrast to Top-Down, the basic process of the Bottom-Up algorithm is
merging where the finest segments are merged continuously until some thresholds
are violated. The main problem for Sliding Window is that it can only “look
forward” but not “look backward”. The main problem for Top-Down is that it lacks
the ability of merging while the main problem for Bottom-Up is that it lacks the
ability of splitting. Therefore, hybrid algorithms which take advantages of the three
generic algorithms by modifications and combinations are often more preferable in
practice [43, 48, 57, 89].
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