Anomaly score calculation for multidimensional data set I'm currently using anomaly score calculation methods which work on single dimensional data sets (Timestamp &amp; Value). I would like to calculate the same single anomaly score for multidimensional data set (Timestamp &amp; Value 1 &amp; Value 2 &amp; ..... &amp; Value N). 

What method could compare all the values and calculate anomaly score based upon the relationship between them?
One very simple yet efficient way, is auto-regression, which means, you train a regressor on past data, and predict the future, if the prediction is off by too much - then you call it an anomaly.

to make it more formal, if we denote $\vec{v}_{t0}$  as your data at timestamp $t_0$, we train a regression model $R$ that learns from $\{v_{t_0},\dots,v_{t_{n-1}}\}$.

Your anomaly score is a function of $R({v_{t_0},\dots,v_{t_{n-1}}})-v_{t_n}$