We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependencies. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs,...

A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is based on the Mahalanobis distance between ...

We develop a procedure for analyzing multivariate nonstationary time series using the SLEX library (smooth localized complex exponentials), which is a collection of bases, each basis consisting of waveforms that are orthogonal and time-localized versions of the Fourier complex exponentials. Under the SLEX framework, we build a family of multivariate models that can explicitly characterize the t...

Medical time-series data often contain sets of closely related, non-orthogonal channel – for example transcutaneous O2 and CO2 or mean, systolic and diastolic blood pressures. It is desirable when summarizing such sets of data to select a single set of time-periods dividing the data into segments of distinctive character. This can be achieved using an extension of existing bottom-up segmentatio...

One of the limiting factors in training data-driven, rare-event prediction algorithms is scarcity events interest resulting an extreme imbalance data. There have been many methods introduced literature for overcoming this issue; simple data manipulation through undersampling and oversampling, utilizing cost-sensitive learning algorithms, or by generating synthetic points following distribution ...