Causal Analytics in IIoT – AI That Knows What Causes What, and When
Figure 1: Granger causality test
Researchers extended this framework, e.g.
to allow for analysis of multiple time series
generated by nonlinear models, for lagging
the cause and effect variables and for causal
graphical models for better handling of
latent variables.
The application of TE to empirical analytics
has been substantial in areas of biomedicine
and climate science. However, further
developments were needed to help
overcome
shortcomings
related
to
26
unreliability and a lack of accuracy . Au
Sable’s work on improving the reliability of
TE, combined with other Au Sable
proprietary algorithms, have led to the
development of an algorithmic approach to
causal analytics that can process IoT event
data and provide reliable results. This means
that Causal Analytics can now be applied to
real-world scenarios with non-experimental
data.
Transfer Entropy 24 25 (TE) is a later
implementation of the principle that causes
must precede and predict their effects. TE
improves on Granger in that it directly caters
for nonlinear interactions and helps
minimize problems of noisy data. TE is a
model-free and non-parametric measure of
directed information flow from one variable
to another.
24 Transfer Entropy https://en.wikipedia.org/wiki/Transfer_entropy
25 Transfer entropy between multivariate time series https://www.sciencedirect.com/science/article/pii/S1007570416305020
26
Progress in Root Cause and Fault Propagation Analysis of Large-Scale Industrial Processes
https://www.hindawi.com/journals/jcse/2012/478373/
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June 2018