CA2471013A1 - Method and system for analyzing and predicting the behavior of systems - Google Patents
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3065—Monitoring arrangements determined by the means or processing involved in reporting the monitored data
- G06F11/3072—Monitoring arrangements determined by the means or processing involved in reporting the monitored data where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting
- G06F11/3082—Monitoring arrangements determined by the means or processing involved in reporting the monitored data where the reporting involves data filtering, e.g. pattern matching, time or event triggered, adaptive or policy-based reporting the data filtering being achieved by aggregating or compressing the monitored data
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3006—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- 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
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/327—Alarm or error message display
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3452—Performance evaluation by statistical analysis
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- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
A monitoring system including a baseline model that automatically captures and models normal system behavior, a correlation model that employs multivariate autoregression analysis to detect abnormal system behavior, and an alarm service that weight and scores a variety of alerts to determine an alarm status and implement appropriate response actions. The baseline model decomposes the input variables into a number of components representing relatively predictable behaviors so that the erratic component e(t) may be isolated for further processing. These components include a global trend component, a cyclical component, and a seasonal component. Modeling and continually updating these components separately permits a more accurate identification of the erratic component of the input variable, which typically reflects abnormal patterns when they occur.
Claims (43)
1. A method for analyzing and predicting the behavior of a system, comprising the steps of:
continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the time-based baseline mean for the selected input variable;
computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial.
continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the time-based baseline mean for the selected input variable;
computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial.
2. The method of claim 1, further comprising the step of updating the time-based baseline mean and variance for the selected input variable based on the measurement received for the selected input value for the current time trial.
3. The method of claim 2, comprising the step of repeating the steps of claims 1 and 2 for multiple input variables.
4. The method of claim 3, further comprising the step of continually repeating the steps of claims 1, 2, and 3 for multiple current time trials.
5. The method of claim 1, wherein the step of determining the alert status for the imputed estimate comprises the steps of:
computing a confidence value associated with the imputed estimate;
computing a threshold value for the imputed estimate based on the confidence value;
computing an imputed estimate alert value reflecting a difference between the imputed estimate and the erratic component . for the selected input variable to the threshold value; and determining an alert status for the imputed estimate by comparing the alert value to the threshold value.
computing a confidence value associated with the imputed estimate;
computing a threshold value for the imputed estimate based on the confidence value;
computing an imputed estimate alert value reflecting a difference between the imputed estimate and the erratic component . for the selected input variable to the threshold value; and determining an alert status for the imputed estimate by comparing the alert value to the threshold value.
6. The method of claim 5, wherein:
the confidence value comprises a standard error associated with the imputed estimate; and the threshold value is based on the standard error and a user-defined configuration parameter.
the confidence value comprises a standard error associated with the imputed estimate; and the threshold value is based on the standard error and a user-defined configuration parameter.
7. The method of claim 2, wherein the step of computing the time-based baseline mean and variance for the selected input variable comprises the steps of:
decomposing the signature for the input variable into components;
computing a mean and variance for each component;
combining the means for the components to obtain the time-based baseline mean; and combining the variances for the components to obtain the time-based baseline variance.
decomposing the signature for the input variable into components;
computing a mean and variance for each component;
combining the means for the components to obtain the time-based baseline mean; and combining the variances for the components to obtain the time-based baseline variance.
8. The method of claim 7, wherein the step of decomposing the signature for the input variable into components comprises the steps of:
defining a repeating cycle for the historical measurements;
dividing the cycle into a plurality of contiguous time periods wherein each cycle comprises a similar set of time periods, each time period having a corresponding time index;
computing a global trend component for the selected input variable reflecting measurements received for the selected input variable for temporally contiguous time indices; and computing a cyclical component for the selected input variable reflecting data accumulated across multiple cycles for each time index.
defining a repeating cycle for the historical measurements;
dividing the cycle into a plurality of contiguous time periods wherein each cycle comprises a similar set of time periods, each time period having a corresponding time index;
computing a global trend component for the selected input variable reflecting measurements received for the selected input variable for temporally contiguous time indices; and computing a cyclical component for the selected input variable reflecting data accumulated across multiple cycles for each time index.
9. The method of claim 2, wherein the step of updating the time-based baseline mean and variance for the selected input variable comprises the steps of:
computing an updated baseline mean based on a weighted sum comprising the baseline mean for the selected input variable and the measurement for the current time trial for the selected input variable; and computing an updated baseline variance based on a weighted sum comprising the baseline variance for the selected input variable and the measurement for the current time trial for the selected input variable.
computing an updated baseline mean based on a weighted sum comprising the baseline mean for the selected input variable and the measurement for the current time trial for the selected input variable; and computing an updated baseline variance based on a weighted sum comprising the baseline variance for the selected input variable and the measurement for the current time trial for the selected input variable.
10. The method of claim 7, wherein the step of updating the time-based baseline mean and variance for the selected input variable comprises the steps of:
computing an updated time-based baseline mean by:
computing an updated mean for each component based on a weighted sum comprising the baseline mean for the component and the measurement received for the selected input variable for the current time trial, and summing the updated means for the components; and computing an updated time-based baseline variance by:
computing an updated variance for each component based on a weighted sum comprising the baseline variance for the component and the measurement received for the selected input variable for the current time trial, and summing the updated variances for the components.
computing an updated time-based baseline mean by:
computing an updated mean for each component based on a weighted sum comprising the baseline mean for the component and the measurement received for the selected input variable for the current time trial, and summing the updated means for the components; and computing an updated time-based baseline variance by:
computing an updated variance for each component based on a weighted sum comprising the baseline variance for the component and the measurement received for the selected input variable for the current time trial, and summing the updated variances for the components.
11. The method of claim 4, further comprising the steps of:
receiving imputed estimate alerts corresponding to multiple input measurements;
weighting the alerts;
computing an alert score based on the weighted alerts; and determining whether to activate an alarm condition based on the alert score.
receiving imputed estimate alerts corresponding to multiple input measurements;
weighting the alerts;
computing an alert score based on the weighted alerts; and determining whether to activate an alarm condition based on the alert score.
12. A computer storage medium storing computer-executable instruction for performing the method of claim 1.
13. An apparatus configured to perform the method of claim 1.
14. A method for analyzing and predicting the behavior of a system, comprising the steps of:
continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the baseline mean for the selected input variable;
computing a forecast estimate for the selected input variable based on erratic components computed for other input variables and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and determining an alert status for the forecast estimate.
continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the baseline mean for the selected input variable;
computing a forecast estimate for the selected input variable based on erratic components computed for other input variables and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and determining an alert status for the forecast estimate.
15. The method of claim 14, further comprising the step of updating the time-based baseline mean and variance for the selected input variable based on the measurement received for the selected input value for the current time trial.
16. The method of claim 15, comprising the step of repeating the steps of claims 14 and 15 for multiple input variables.
17. The method of claim 16, further comprising the step of continually repeating the steps of claims 14, 15, and 16 for multiple future forecasts.
18. The method of claim 17, further comprising the step of continually repeating the steps of claims 14, 15, 16, and 17 for multiple current time trials.
19. The method of claim 15, wherein the step of determining the alert status for the forecast estimate comprises the steps of:
computing a threshold value for the forecast estimate; and determining an alert status for the forecast estimate by comparing the forecast:
estimate to the threshold value.
computing a threshold value for the forecast estimate; and determining an alert status for the forecast estimate by comparing the forecast:
estimate to the threshold value.
20. The method of claim 19, wherein the threshold value is based on the time-based baseline variance for the selected input variable and a user-defined configuration parameter.
21. The method of claim 15, wherein the step of computing the time-based baseline mean and variance for the selected input variable comprises the steps of:
decomposing the signature for the input variable into components;
computing a mean and variance for each component;
combining the means for the components to obtain the time-based baseline mean; and combining the variances for the components to obtain the time-based baseline variance.
decomposing the signature for the input variable into components;
computing a mean and variance for each component;
combining the means for the components to obtain the time-based baseline mean; and combining the variances for the components to obtain the time-based baseline variance.
22. The method of claim 21, wherein the step of decomposing the signature for the input variable into components comprises the steps of:
defining a repeating cycle for the historical measurements;
dividing the cycle into a plurality of contiguous time periods wherein each cycle comprises a similar set of time periods, each time period having a corresponding time index;
computing a global trend component for the selected input variable reflecting measurements received for the selected input variable for temporally contiguous time indices; and computing a cyclical component for the selected input variable reflecting data accumulated across multiple cycles for each time index.
defining a repeating cycle for the historical measurements;
dividing the cycle into a plurality of contiguous time periods wherein each cycle comprises a similar set of time periods, each time period having a corresponding time index;
computing a global trend component for the selected input variable reflecting measurements received for the selected input variable for temporally contiguous time indices; and computing a cyclical component for the selected input variable reflecting data accumulated across multiple cycles for each time index.
23. The method of claim 15, wherein the step of updating the time-based baseline mean and variance for the selected input variable comprises the steps of:
computing an updated baseline mean based on a weighted sum comprising the baseline mean for the selected input variable and the measurement for the current time trial for the selected input variable; and computing an updated baseline variance based on a weighted sum comprising the baseline variance for the selected input variable and the measurement for the current time trial for the selected input variable.
computing an updated baseline mean based on a weighted sum comprising the baseline mean for the selected input variable and the measurement for the current time trial for the selected input variable; and computing an updated baseline variance based on a weighted sum comprising the baseline variance for the selected input variable and the measurement for the current time trial for the selected input variable.
24. The method of claim 21, wherein the step of updating the time-based baseline mean and variance for the selected input variable comprises the steps of:
computing an updated time-based baseline mean by:
computing an updated mean for each component based on a weighted sum comprising the baseline mean for the component and the measurement received for the selected input variable for the current time trial, and summing the updated means for the components; and computing an updated time-based baseline variance by:
computing an updated variance for each component based on a weighted sum comprising the baseline variance for the component and the measurement received for the selected input variable for the current time trial, and summing the updated variances for the components.
computing an updated time-based baseline mean by:
computing an updated mean for each component based on a weighted sum comprising the baseline mean for the component and the measurement received for the selected input variable for the current time trial, and summing the updated means for the components; and computing an updated time-based baseline variance by:
computing an updated variance for each component based on a weighted sum comprising the baseline variance for the component and the measurement received for the selected input variable for the current time trial, and summing the updated variances for the components.
25. The method of claim 18, further comprising the steps of:
receiving imputed estimate alerts corresponding to multiple input measurements;
weighting the alerts;
computing an alert score based on the weighted alerts; and determining whether to activate an alarm condition based on the alert score.
receiving imputed estimate alerts corresponding to multiple input measurements;
weighting the alerts;
computing an alert score based on the weighted alerts; and determining whether to activate an alarm condition based on the alert score.
26. A computer storage medium storing computer-executable instruction for performing the method of claim 18.
27. An apparatus configured to perform the method of claim 18.
28. A method for analyzing and predicting the behavior of a system, comprising the steps of:
continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the baseline mean for the selected input variable;
computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current, time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables;
determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial;
computing a forecast estimate for the selected input variable based on erratic components computed for other input variables and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and determining an alert status for the forecast estimate.
continually receiving measurements defining signatures for a plurality of input variables reflecting the behavior of the system, each signature comprising a time series of measurements including historical measurements for past time trials and a current measurement for a current time trial;
computing a time-based baseline mean and variance for a selected input variable based on the historical measurements for the selected input variable;
computing an erratic component for the selected input variable by comparing the measurement for the selected input value for the current time trial to the baseline mean for the selected input variable;
computing an imputed estimate for the selected input variable based on erratic components computed for other input variables for the current, time trial and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables;
determining an alert status for the imputed estimate based on the imputed estimate and the erratic component for the input variable for the current time trial;
computing a forecast estimate for the selected input variable based on erratic components computed for other input variables and learned parameters reflecting observed relationships between the erratic component for the selected input variable and the erratic components for the other input variables; and determining an alert status for the forecast estimate.
29. The method of claim 28, further comprising the step of updating the time-based baseline mean and variance for the selected input variable based on the measurement received for the selected input value for the current time trial.
30. The method of claim 29, comprising the step of repeating the steps of claims 28 and 28 for multiple input variables.
31. The method of claim 30, further comprising the step of continually repeating the steps of claims 28, 29, and 30 for multiple future forecasts.
32. The method of claim 31, further comprising the step of continually repeating the steps of claims 28, 29, 30, and 31 for multiple current time trials.
33. The method of claim 32, wherein the step of determining the alert status for the imputed estimate comprises the steps of:
computing a confidence value for the imputed estimate;
computing a threshold value for the imputed estimate based on the confidence value;
computing an imputed estimate alert value reflecting a difference between the imputed estimate and the erratic component for the selected input variable to the threshold value for the imputed estimate; and determining an alert status for the imputed estimate by comparing the alert value to the threshold value for the imputed estimate.
computing a confidence value for the imputed estimate;
computing a threshold value for the imputed estimate based on the confidence value;
computing an imputed estimate alert value reflecting a difference between the imputed estimate and the erratic component for the selected input variable to the threshold value for the imputed estimate; and determining an alert status for the imputed estimate by comparing the alert value to the threshold value for the imputed estimate.
34. The method of claim 33, wherein:
the confidence value for the imputed estimate comprises a standard error associated with the imputed estimate; and the threshold value for the imputed estimate is based on the standard error and a user-defined configuration parameter.
the confidence value for the imputed estimate comprises a standard error associated with the imputed estimate; and the threshold value for the imputed estimate is based on the standard error and a user-defined configuration parameter.
35. The method of claim 34, wherein the step of determining the alert status for the forecast estimate comprises the steps of:
computing a threshold value for the forecast estimate; and determining an alert status for the forecast estimate by comparing the forecast estimate to the threshold value.
computing a threshold value for the forecast estimate; and determining an alert status for the forecast estimate by comparing the forecast estimate to the threshold value.
36. The method of claim 35, wherein the threshold value is based on the time-based baseline variance for the selected input variable and a user-defined configuration parameter.
37. The method of claim 36, wherein the step of computing the time-based baseline mean and variance for the selected input variable comprises the steps of:
decomposing the signature for the input variable into components;
computing a mean and variance for each component;
combining the means for the components to obtain the time-based baseline mean; and combining the variances for the components to obtain the time-based baseline variance.
decomposing the signature for the input variable into components;
computing a mean and variance for each component;
combining the means for the components to obtain the time-based baseline mean; and combining the variances for the components to obtain the time-based baseline variance.
38. The method of claim 37, wherein the step of decomposing the signature for the input variable into components comprises the steps of:
defining a repeating cycle for the historical measurements;
dividing the cycle into a plurality of contiguous time periods wherein each cycle comprises a similar set of time periods, each time period having a corresponding time index;
computing a global trend component for the selected input variable reflecting measurements received for the selected input variable for temporally contiguous time indices; and computing a cyclical component for the selected input variable reflecting data accumulated across multiple cycles for each time index.
defining a repeating cycle for the historical measurements;
dividing the cycle into a plurality of contiguous time periods wherein each cycle comprises a similar set of time periods, each time period having a corresponding time index;
computing a global trend component for the selected input variable reflecting measurements received for the selected input variable for temporally contiguous time indices; and computing a cyclical component for the selected input variable reflecting data accumulated across multiple cycles for each time index.
39. The method of claim 38, wherein the step of updating the time-based baseline mean and variance for the selected input variable comprises the steps of:
computing an updated baseline mean based on a weighted sum comprising the baseline mean for the selected input variable and the measurement for the current time trial for the selected input variable; and computing an updated baseline variance based on a weighted sum comprising the baseline variance for the selected input variable and the measurement for the current time trial for the selected input variable.
computing an updated baseline mean based on a weighted sum comprising the baseline mean for the selected input variable and the measurement for the current time trial for the selected input variable; and computing an updated baseline variance based on a weighted sum comprising the baseline variance for the selected input variable and the measurement for the current time trial for the selected input variable.
40. The method of claim 39, wherein the step of updating the time-based baseline mean and variance for the selected input variable comprises the steps of:
computing an updated time-based baseline mean by:
computing an updated mean for each component based on a weighted sum comprising the baseline mean for the component and the measurement received for the selected input variable for the current time trial, and summing the updated means for the components; and computing an updated time-based baseline variance by:
computing an updated variance for each component based on a weighted sum comprising the baseline variance for the component and the measurement received for the selected input variable for the current time trial, and summing the updated variances for the components.
computing an updated time-based baseline mean by:
computing an updated mean for each component based on a weighted sum comprising the baseline mean for the component and the measurement received for the selected input variable for the current time trial, and summing the updated means for the components; and computing an updated time-based baseline variance by:
computing an updated variance for each component based on a weighted sum comprising the baseline variance for the component and the measurement received for the selected input variable for the current time trial, and summing the updated variances for the components.
41. The method of claim 40, further comprising the steps of:
receiving imputed estimate and forecast estimate alerts corresponding to multiple input measurements;
weighting the alerts;
computing an alert score based on the weighted alerts; and determining whether to activate an alarm condition based on the alert score.
receiving imputed estimate and forecast estimate alerts corresponding to multiple input measurements;
weighting the alerts;
computing an alert score based on the weighted alerts; and determining whether to activate an alarm condition based on the alert score.
42. A computer storage medium storing computer-executable instruction for performing the method of claim 41.
43. An apparatus configured to perform the method of claim 41.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US34231201P | 2001-12-19 | 2001-12-19 | |
| US60/342,312 | 2001-12-19 | ||
| PCT/US2002/040837 WO2003054704A1 (en) | 2001-12-19 | 2002-12-19 | Method and system for analyzing and predicting the behavior of systems |
Publications (2)
| Publication Number | Publication Date |
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| CA2471013A1 true CA2471013A1 (en) | 2003-07-03 |
| CA2471013C CA2471013C (en) | 2011-07-26 |
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| CA2471013A Expired - Fee Related CA2471013C (en) | 2001-12-19 | 2002-12-19 | Method and system for analyzing and predicting the behavior of systems |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US7280988B2 (en) |
| EP (1) | EP1468361A1 (en) |
| AU (1) | AU2002360691A1 (en) |
| CA (1) | CA2471013C (en) |
| WO (1) | WO2003054704A1 (en) |
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- 2002-12-19 AU AU2002360691A patent/AU2002360691A1/en not_active Abandoned
- 2002-12-19 WO PCT/US2002/040837 patent/WO2003054704A1/en not_active Application Discontinuation
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| CA2471013C (en) | 2011-07-26 |
| US7280988B2 (en) | 2007-10-09 |
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| EP1468361A1 (en) | 2004-10-20 |
| US20030139905A1 (en) | 2003-07-24 |
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