1 research outputs found
Applying nature-inspired optimization algorithms for selecting important timestamps to reduce time series dimensionality
Time series data account for a major part of data supply available today.
Time series mining handles several tasks such as classification, clustering,
query-by-content, prediction, and others. Performing data mining tasks on raw
time series is inefficient as these data are high-dimensional by nature.
Instead, time series are first pre-processed using several techniques before
different data mining tasks can be performed on them. In general, there are two
main approaches to reduce time series dimensionality, the first is what we call
landmark methods. These methods are based on finding characteristic features in
the target time series. The second is based on data transformations. These
methods transform the time series from the original space into a reduced space,
where they can be managed more efficiently. The method we present in this paper
applies a third approach, as it projects a time series onto a lower-dimensional
space by selecting important points in the time series. The novelty of our
method is that these points are not chosen according to a geometric criterion,
which is subjective in most cases, but through an optimization process. The
other important characteristic of our method is that these important points are
selected on a dataset-level and not on a single time series-level. The direct
advantage of this strategy is that the distance defined on the low-dimensional
space lower bounds the original distance applied to raw data. This enables us
to apply the popular GEMINI algorithm. The promising results of our experiments
on a wide variety of time series datasets, using different optimizers, and
applied to the two major data mining tasks, validate our new method.Comment: 13 pages, Evolving Systems (2017).
https://link.springer.com/article/10.1007/s12530-017-9207-7#citea