The HHT uses the Empirical Mode Decomposition (EMD) method to decompose a time series into a collection of the Intrinsic Mode Functions (IMFs), and uses the Hilbert Spectral Analysis (HSA) to obtain instantaneous frequency data.
In the [24] literature, according to the changes of slope, the authors described the shape characteristics of time series as a collection of seven variables {declining rapidly, keeping down, declining slowly, horizontal, rising slowly, keeping rising, rising rapidly}, and they were denoted as M = {-3, -2, -1, 0, 1, 2, 3} corresponding to the above-described mode.
Forest disturbance information was recorded with a Landsat time series visualization and data collection tool that incorporates ancillary high-resolution data.
To improve the scalability of the model the authors use principal component analysis (PCA) as follows: 1) use PCA to find the main principal components that can approximate the time series for the entire collection of videos and 2) apply ARMA modeling to the principal components instead of the individual time series.
A subset used a pre-/post (or time series) approach to data collection, allowing researchers to track changes before, after, and sometimes during an intervention.
The relevance of the decadal scale for marine ecological changes has become evident in the last 30 years, when multi decadal time series of in situ collections provided records long enough for patterns to be statistically discernible.
In the statistical analysis presented here, we consider a particular kind of serological time series with repeated, periodic collections of population-representative serum.
One important information retrieval task is the search for patterns in the huge collection of time series data.
However, if the panel data set is viewed as a collection of time series it is not difficult to extend the hidden Markov models used in time series analysis to the panel data setting.
Mark these clusters on the map if these basic granularities are geographic based, or display them as a collection of time series curves if they are still time based.
Given a collection of time series data and a formal spatio-temporal specification the model checker Mudi (http://mudi.modelchecking.org) determines probabilistically if the formal specification holds for the computational model or not.
