Causal Discovery from Sparse Time-Series Data Using Echo State Network
Abstract
Causal discovery between collections of time-series data can helpdiagnose causes of symptoms and hopefully prevent faults before
they occur. However, reliable causal discovery can be very chal-
lenging, especially when the data acquisition rate varies (i.e., non-
uniform data sampling), or in the presence of missing data points
(e.g., sparse data sampling). To address these issues, we propose
a new system comprised of two parts, the first part fills in missing data
with a Gaussian Process Regression, and the second part lever-
ages an Echo State Network, which is a type of reservoir computer
(i.e., used for chaotic system modelling) for Causal discovery.
We evaluate the performance of our proposed system against
three other off-the-shelf causal discovery algorithms, namely, struc-
tural expectation maximization, sub-sampled linear auto-regression
absolute coefficients, and multivariate Granger Causality with vector
auto-regressive using the Tennessee Eastman chemical dataset;
we report on their corresponding Matthews Correlation Coefficient
(MCC) and Receiver Operating Characteristic curves (ROC) and
show that the proposed system outperforms existing algorithms,
demonstrating the viability of our approach to discover causal re-
lationships in a complex system with missing entries.