Integrated approach to model time series dynamics in complex physical systems

Patent metadata

Field	Value
Publication number	US-9245235-B2
Application number	US-201314050945-A
Country	US
Kind code	B2
Filing date	Oct 10, 2013
Priority date	Oct 12, 2012
Publication date	Jan 26, 2016
Grant date	Jan 26, 2016

Abstract

A system and method for analysis of complex systems which includes determining model parameters based on time series data, further including profiling a plurality of types of data properties to discover complex data properties and dependencies; classifying the data dependencies into predetermined categories for analysis; and generating a plurality of models based on the discovered properties and dependencies. The system and method may analyze, using a processor, the generated models based on a fitness score determined for each model to generate a status report for each model; integrate the status reports for each model to determine an anomaly score for the generated models; and generate an alarm when the anomaly score exceeds a predefined threshold.

First claim

What is claimed is:

1. A method for analyzing complex systems, comprising:
determining model parameters based on time series data, including;
profiling a plurality of types of data properties of one or more complex physical systems to discover complex data properties and dependencies;
classifying the data dependencies into predetermined categories for analysis; and
generating a plurality of models, including a vector-autoregressive (VAR) model comprising one or more feature vectors, based on the discovered properties and dependencies;

analyzing, using a processor, the generated models based on a fitness score determined for each model to generate a status report for each model, the analyzing including detecting anomalies by comparing a predicted feature vector value with a current feature vector value from the VAR model;
integrating the status reports for each model to determine an anomaly score for the generated models; and
generating an alarm when the anomaly score exceeds a predefined threshold.

2. The method according to claim 1 , wherein each model whose anomaly score exceeds the predefined threshold is pruned out of the system.

3. The method according to claim 1 , wherein the data dependencies are classified into layers from low to high orders.

4. The method according to claim 3 , wherein at least one of single attribute analysis, pairwise analysis, group-wise analysis, and all attributes analysis is carried out in the layers.

5. The method according to claim 1 , wherein the fitness score is determined based on the goodness of fit for each time series.

6. The method according to claim 1 , wherein the discovered properties are analyzed in parametric form.

7. The method according to claim 1 , wherein a root cause of an anomaly is determined based on model IDs and attribute IDs from the status reports.

8. A system for analyzing complex systems, comprising:
a modeling module configured to determine model parameters based on time series data, including;
a profiler module configured to profile a plurality of types of data properties of one or more complex physical systems to discover complex data properties and dependencies;
a classifier module configured to classify the data dependencies into predetermined categories for analysis; and
a generator module configured to generate a plurality of models, including a vector-autoregressive (VAR) model comprising one or more feature vectors, based on the discovered properties and dependencies;

an analysis module configured to analyze, using a processor, the generated models based on a fitness score determined for each model to generate a status report for each model, the analysis module being further configured to detect anomalies by comparing a predicted feature vector value with a current feature vector value from the VAR model;
an integration module configured to integrate the status reports for each model to determine an anomaly score for the generated models; and
an alarm generation module configured to generate an alarm when the anomaly score exceeds a predefined threshold.

9. The system according to claim 8 , wherein each model whose anomaly score exceeds the predefined threshold is pruned out of the system.

10. The system according to claim 8 , wherein the data dependencies are classified into layers from low to high orders.

11. The system according to claim 10 , wherein at least one of single attribute analysis, pairwise analysis, group-wise analysis, and all attributes analysis is carried out in the layers.

12. The system according to claim 8 , wherein the fitness score is determined based on the goodness of fit for each time series.

13. The system according to claim 8 , wherein the discovered properties are analyzed in parametric form.

14. The system according to claim 8 , wherein a root cause of an anomaly is determined based on model IDs and attribute IDs from the status reports.

Assignees

• Nec Lab America Inc

Inventors

• Chen Haifeng
• Ding Min
• Liu Bin
• Sharma Abhishek
• Yoshihira Kenji
• Jiang Guofei

Classifications

• G06N5/048

  Fuzzy inferencing · CPC title

• G06F11/3072

  where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting · CPC title

• G06N99/005 Primary

  Physics · mapped topic

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06F11/3055

  Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available (error or fault processing without redundancy G06F11/0703; error detection or correction by redundancy in data representation G06F11/08; error detection or correction of the data by redundancy in operations G06F11/14; error detection or correction by redundancy in hardware G06F11/16) · CPC title