  We frequently encounter multiple series that are temporally correlated in our
surroundings, such as EEG data to examine alterations in brain activity or
sensors to monitor body movements. Segmentation of multivariate time series
data is a technique for identifying meaningful patterns or changes in the time
series that can signal a shift in the system's behavior. However, most
segmentation algorithms have been designed primarily for univariate time
series, and their performance on multivariate data remains largely
unsatisfactory, making this a challenging problem. In this work, we introduce a
novel approach for multivariate time series segmentation using conditional
independence (CI) graphs. CI graphs are probabilistic graphical models that
represents the partial correlations between the nodes. We propose a domain
agnostic multivariate segmentation framework `$\texttt{tGLAD}$' which draws a
parallel between the CI graph nodes and the variables of the time series.
Consider applying a graph recovery model $\texttt{uGLAD}$ to a short interval
of the time series, it will result in a CI graph that shows partial
correlations among the variables. We extend this idea to the entire time series
by utilizing a sliding window to create a batch of time intervals and then run
a single $\texttt{uGLAD}$ model in multitask learning mode to recover all the
CI graphs simultaneously. As a result, we obtain a corresponding temporal CI
graphs representation. We then designed a first-order and second-order based
trajectory tracking algorithms to study the evolution of these graphs across
distinct intervals. Finally, an `Allocation' algorithm is used to determine a
suitable segmentation of the temporal graph sequence. $\texttt{tGLAD}$ provides
a competitive time complexity of $O(N)$ for settings where number of variables
$D<<N$. We demonstrate successful empirical results on a Physical Activity
Monitoring data.
