We introduce a spatio-temporal model to represent development of atmospheric pollution in an urban area. An important element this is that recorded measurements are often incomplete which undermines time-series approaches. identify the multiple imputation by chained equation (MICE) method as effective complete data sequences synthetically. Following on from this, we develop vector autoregressiv...

In this work, we introduce TA4L, a new efficient algorithm to transform multivariate time series into Lexicographical Symbolic Time Interval Sequences (LSTISs), that is, sequences ready feed time-interval related pattern (TIRP) mining algorithms. The ultimate goal is make explicit the embedded, ad-hoc pre-processes TIRP algorithms while offering an solution for required pre-processing. On one h...

We study inference on the common stochastic trends in a non-stationary, $N$-variate time series $y_{t}$, possible presence of heavy tails. propose novel methodology which does not require any knowledge or estimation tail index, even as to whether certain moments (such variance) exist not, and develop an estimator number $m$ based eigenvalues sample second moment matrix $y_{t}$. rates such eigen...

This work gives an overview of statistical analysis for some models multivariate discrete-valued (MDV) time series. We present observation-driven and based on higher-order Markov chains. Several extensions are highlighted including non-stationarity, network autoregressions, conditional non-linear autoregressive models, robust estimation, random fields spatio-temporal models.