In the first step, a multivariate time series of the kinematics and marker trajectories (left plot, with only three variables displayed) is mapped to a univariate time series (middle plot) with some function f. In the second step a peak detection algorithm g is applied to convert univariate time series into a binary time series (0 when no event and 1 otherwise).</p
Time-series data often experiences abrupt changes in structure. If the time-series is to be modelled...
Most time series comparison algorithms attempt to discover what the members of a set of time series ...
A multivariate time series is a time-indexed sequence of multidimensional samples. Such a kind of da...
The preprocessing of the raw data consisted of three steps: stop detection, spatial aggregation, and...
<p>The basic principle of data reduction to accelerate time series plotting: (a) A computer display ...
High dimension complex dynamical systems, such as those found in physiological processes, produce ti...
Classifying multivariate time series is often dealt with by transforming the numeric series into lab...
There is nowadays a constant flux of data being generated and collected in all types of real world ...
The purpose of this project was to expand the applications of time series prediction and action reco...
Abstract. An important task in signal processing and temporal data mining is time series segmentatio...
Perhaps the single most important lesson to be drawn from the study of non-linear dynamical sys-tems...
<p>Schematic view of the algorithm used to obtain the probability distribution of the exit times (τ)...
PosterNational audienceThis paper proposes an extension of the classification trees to time series i...
Change-point detection is useful in many areas and there are several algorithms developed to cater s...
In the field of Big Data, multivariate time series collect high dimensional data of observed subject...
Time-series data often experiences abrupt changes in structure. If the time-series is to be modelled...
Most time series comparison algorithms attempt to discover what the members of a set of time series ...
A multivariate time series is a time-indexed sequence of multidimensional samples. Such a kind of da...
The preprocessing of the raw data consisted of three steps: stop detection, spatial aggregation, and...
<p>The basic principle of data reduction to accelerate time series plotting: (a) A computer display ...
High dimension complex dynamical systems, such as those found in physiological processes, produce ti...
Classifying multivariate time series is often dealt with by transforming the numeric series into lab...
There is nowadays a constant flux of data being generated and collected in all types of real world ...
The purpose of this project was to expand the applications of time series prediction and action reco...
Abstract. An important task in signal processing and temporal data mining is time series segmentatio...
Perhaps the single most important lesson to be drawn from the study of non-linear dynamical sys-tems...
<p>Schematic view of the algorithm used to obtain the probability distribution of the exit times (τ)...
PosterNational audienceThis paper proposes an extension of the classification trees to time series i...
Change-point detection is useful in many areas and there are several algorithms developed to cater s...
In the field of Big Data, multivariate time series collect high dimensional data of observed subject...
Time-series data often experiences abrupt changes in structure. If the time-series is to be modelled...
Most time series comparison algorithms attempt to discover what the members of a set of time series ...
A multivariate time series is a time-indexed sequence of multidimensional samples. Such a kind of da...