A system for predicting critical transitions, the system comprising: an electronic circuit having a plurality of components, each component having a time series of voltage measurements; and one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of: transforming the time series of voltage measurements from the plurality of components of the electronic circuit into a set of symbolic multivariate time series; determining a transfer entropy measure between two time series in the set of symbolic multivariate time series over a sliding time window; determining an associative transfer entropy measure, wherein the ATE measure is comprised of an ATE+ positive influence class and an ATE- negative influence class, and wherein the ATE measure captures information transfer for a state association D k, between a variable x j and a variable x i, for i ≠ j according to the following: T x j → x i D k = ∑ x i, t + 1 x i, t x j, t ∈ D k p x i, t + 1 x i, t x j, t log p x i, t + 1 x i, t x j, t p x i, t + 1 x i, t, where Σ represents a summation, t represents a unit of time, p represents a joint probability of the variables, and | represents a conditional probability, wherein the variables x j and x i represent states of at least one component of the electric circuit; estimating ATE+, TE, and ATE- trajectories over time; and based on the estimated ATE+, TE, and ATE- trajectories, predicting instability in the electronic circuit. an electronic circuit having a plurality of components, each component having a time series of voltage measurements; and one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of: transforming the time series of voltage measurements from the plurality of components of the electronic circuit into a set of symbolic multivariate time series; determining a transfer entropy measure between two time series in the set of symbolic multivariate time series over a sliding time window; determining an associative transfer entropy measure, wherein the ATE measure is comprised of an ATE+ positive influence class and an ATE- negative influence class, and wherein the ATE measure captures information transfer for a state association D k, between a variable x j and a variable x i, for i ≠ j according to the following: T x j → x i D k = ∑ x i, t + 1 x i, t x j, t ∈ D k p x i, t + 1 x i, t x j, t log p x i, t + 1 x i, t x j, t p x i, t + 1 x i, t, where Σ represents a summation, t represents a unit of time, p represents a joint probability of the variables, and | represents a conditional probability, wherein the variables x j and x i represent states of at least one component of the electric circuit; estimating ATE+, TE, and ATE- trajectories over time; and based on the estimated ATE+, TE, and ATE- trajectories, predicting instability in the electronic circuit. transforming the time series of voltage measurements from the plurality of components of the electronic circuit into a set of symbolic multivariate time series; determining a transfer entropy measure between two time series in the set of symbolic multivariate time series over a sliding time window; determining an associative transfer entropy measure, wherein the ATE measure is comprised of an ATE+ positive influence class and an ATE- negative influence class, and wherein the ATE measure captures information transfer for a state association D k, between a variable x j and a variable x i, for i ≠ j according to the following: T x j → x i D k = ∑ x i, t + 1 x i, t x j, t ∈ D k p x i, t + 1 x i, t x j, t log p x i, t + 1 x i, t x j, t p x i, t + 1 x i, t, where Σ represents a summation, t represents a unit of time, p represents a joint probability of the variables, and | represents a conditional probability, wherein the variables x j and x i represent states of at least one component of the electric circuit; estimating ATE+, TE, and ATE- trajectories over time; and based on the estimated ATE+, TE, and ATE- trajectories, predicting instability in the electronic circuit.