PREDICTING SYSTEM TRAJECTORIES TOWARD CRITICAL TRANSITIONS
First Claim
1. A system for predicting system trajectories toward critical transitions, the system comprising:
- one or more processors installed as an embedded decision support module in a vehicle entity 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 a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series, wherein the complex system is a heterogeneously networked dynamical system of vehicle entities;
determining a transfer entropy (TE) measure between two time series, wherein the TE measure quantifies an amount of information transfer from a source vehicle entity to a destination vehicle entity in the complex system;
determining an associative transfer entropy (ATE) measure by decomposing the TE measure to associative states of asymmetric, directional information flows, the associative states being an ATE+ positive influence class and an ATE−
negative influence class,wherein an influence from the source vehicle entity on the destination vehicle entity is positively correlated in the ATE+ positive influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is increasing, and an influence from the source vehicle entity on the destination vehicle entity is negatively correlated in the ATE−
negative influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is decreasing;
estimating ATE+, TE, and ATE−
trajectories over time; and
predicting a critical transition in the complex system using at least one of the ATE+, TE, and ATE−
trajectories for analysis of directional information influences among vehicle entities of the complex system to avoid a catastrophic failure of the complex system.
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Abstract
Described is a system for predicting system trajectories toward critical transitions. The system transforms a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series. Then pair-wise time series of a transfer entropy (TE) measure are determined, wherein the TE measure quantifies the amount of information transfer from a source to a destination in the complex system. An associative transfer entropy (ATE) measure is determined which decomposes the pair-wise time series of TE to associative states of asymmetric, directional information flows, wherein the ATE measure is comprised of an ATE+ influence class and a ATE− influence class. The system estimates ATE+, TE, and ATE− trajectories over time, and at least one of the ATE+, TE, and ATE− trajectories is used to predict a critical transition in the complex system.
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Citations
15 Claims
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1. A system for predicting system trajectories toward critical transitions, the system comprising:
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one or more processors installed as an embedded decision support module in a vehicle entity 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 a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series, wherein the complex system is a heterogeneously networked dynamical system of vehicle entities; determining a transfer entropy (TE) measure between two time series, wherein the TE measure quantifies an amount of information transfer from a source vehicle entity to a destination vehicle entity in the complex system; determining an associative transfer entropy (ATE) measure by decomposing the TE measure to associative states of asymmetric, directional information flows, the associative states being an ATE+ positive influence class and an ATE−
negative influence class,wherein an influence from the source vehicle entity on the destination vehicle entity is positively correlated in the ATE+ positive influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is increasing, and an influence from the source vehicle entity on the destination vehicle entity is negatively correlated in the ATE−
negative influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is decreasing;estimating ATE+, TE, and ATE−
trajectories over time; andpredicting a critical transition in the complex system using at least one of the ATE+, TE, and ATE−
trajectories for analysis of directional information influences among vehicle entities of the complex system to avoid a catastrophic failure of the complex system. - View Dependent Claims (2, 3, 4, 5)
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6. A computer-implemented method for predicting system trajectories toward critical transitions, comprising an act of:
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causing one or more processors installed as an embedded decision support module in a vehicle entity to execute instructions stored on a non-transitory memory such that upon execution, the one or more processors perform operations of; transforming a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series, wherein the complex system is a heterogeneously networked dynamical system of vehicle entities; determining a transfer entropy (TE) measure between two time series, wherein the TE measure quantifies an amount of information transfer from a source vehicle entity to a destination vehicle entity in the complex system; determining an associative transfer entropy (ATE) measure by decomposing the TE measure to associative states of asymmetric, directional information flows, the associative states being an ATE+ positive influence class and an ATE−
negative influence class,wherein an influence from the source vehicle entity on the destination vehicle entity is positively correlated in the ATE+ positive influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is increasing, and an influence from the source vehicle entity on the destination vehicle entity is negatively correlated in the ATE−
negative influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is decreasing;estimating ATE+, TE, and ATE−
trajectories over time; andpredicting a critical transition in the complex system using at least one of the ATE+, TE, and ATE−
trajectories for analysis of directional information influences among vehicle entities of the complex system to avoid a catastrophic failure of the complex system. - View Dependent Claims (7, 8, 9, 10)
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11. A computer program product for predicting system trajectories toward critical transitions, the computer program product comprising computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors installed as an embedded support module in a vehicle entity for causing the one or more processors to perform operations of:
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transforming a set of multivariate time series of observables of a complex system into a set of symbolic multivariate time series, wherein the complex system is a heterogeneously networked dynamical system of vehicle entities; determining a transfer entropy (TE) measure between two time series, wherein the TE measure quantifies an amount of information transfer from a source vehicle entity to a destination vehicle entity in the complex system; determining an associative transfer entropy (ATE) measure by decomposing the TE measure to associative states of asymmetric, directional information flows, the associative states being an ATE+ positive influence class and an ATE−
negative influence class,wherein an influence from the source vehicle entity on the destination vehicle entity is positively correlated in the ATE+ positive influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is increasing, and an influence from the source vehicle entity on the destination vehicle entity is negatively correlated in the ATE−
negative influence class such that if a value of the source vehicle entity is increasing, a value of the destination vehicle entity is decreasing;estimating ATE+, TE, and ATE−
trajectories over time; andpredicting a critical transition in the complex system using at least one of the ATE+, TE, and ATE−
trajectories for analysis of directional information influences among vehicle entities of the complex system to avoid a catastrophic failure of the complex system. - View Dependent Claims (12, 13, 14, 15)
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Specification