In this paper, we present an abstract framework for online approximation of time-series data that yields a unified set of algorithms for several popular models: data streams, amnes...
The objective of this paper is to illustrate the application of genetic programming to evolve classifiers for multi-channel time series data. The paper shows how high performance d...
We introduce transformations from time series data to the domain of complex networks which allow us to characterise the dynamics underlying the time series in terms of topological ...
Time series representations are common in MIR applications such as query-by-humming, where a sung query might be represented by a series of ‘notes’ for database retrieval. Whi...
Norman H. Adams, Mark A. Bartsch, Jonah Shifrin, G...
Time series are a data type of utmost importance in many domains such as business management and service monitoring. We address the problem of visualizing large time-related data ...
Ming C. Hao, Umeshwar Dayal, Daniel A. Keim, Tobia...