This paper deals with the exploration of biomedical multivariate time series to construct typical parameter evolution or scenarios.
After adjusting for socioeconomic status and age in our multivariate model, time to childbirth was significantly longer for survivors than controls (HR 0.92, 95% CI 0.87 0.98) (Table 3).
24 For the associations on the multivariate analysis, time to event was defined as the time in days from the start of treatment until definite discontinuation due to ADRs, and then called time to interruption.
For an introduction to multivariate time series analysis, in particular to multivariate AR modeling, we refer to (Lütkepohl 2007) and (Hannan and Deistler 1988).
A forecasting model is then computed by resorting to multivariate time series analysis, in order to predict the future values of a time series by utilizing not only its own historical values, but also information from other cluster-time series.
Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area.
Cobra is programmed in Ox, the object-oriented statistical system, and consists of three modules: Firstly, CobraDgp is derived from the Database class and enables the user to generate a multivariate time series that is subject to multiple breaks in its intercept as well as linear trend.
The dimensionality reduction is that multivariate time series is reduced to univariate time series denoted as center sequences according to the principle of piecewise aggregate approximation.
To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied.
We use multivariate time series econometric model to analyse the temporal evaluation of load factor.
