Huge time-series stream data are collected every day from many areas, and their trends may be impacted by outside events, hence biased from its normal behavior. This phenomenon is ...
Yue Wang, Jie Zuo, Ning Yang, Lei Duan, Hong-Jun L...
More and more data mining algorithms are applied to a large number of long time series issued by many distributed sensors. The consequence of the huge volume of data is that data ...
Raja Chiky, Laurent Decreusefond, Georges Hé...
Pointwise consistent, feasible procedures for estimating contemporaneous linear causal structure from time series data have been developed using multiple conditional independence ...
Time series stored as feature vectors can be indexed by multidimensional index trees like R-Trees for fast retrieval. Due to the dimensionality curse problem, transformations are ...
We propose a new method for detecting activation in functional magnetic resonance imaging (fMRI) data. We project the fMRI time series on a low-dimensional subspace spanned by wave...