Automated analyzers for estimation systems
First Claim
1. In or for an estimation system operable for receiving input values for successive time trials and, for each time trial, computing output values based on the input values and learned parameters and updating the learned parameters to reflect relationships observed among the input and output values, an analyzer operable for performing the steps of:
- (a) receiving historical data comprising samples of the input and output values for a plurality of time trials;
(b) receiving a set of model reduction configuration parameters including specifications for a statistical model that may be implemented by the estimation system;
(c) activating the estimation system to run the historical data on the statistical model to compute the output values and the learned parameters for the statistical model;
(d) analyzing the learned parameters to identify input values that are ineffective for estimating the output values;
(e) reducing the size of the model by eliminating the ineffective input values.
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Accused Products
Abstract
This invention specifies analyzers to be run in conjunction with computer estimation systems, which may be applied to performance monitoring (APM) services. A semi-automated analyzer may be used by a human analyst to periodically evaluate available historical data for establishing a desired set of input measurements, model parameters, and reporting criteria, collectively called configuration parameters, to be used by an estimation system. In addition, a fully automated analyzer may periodically and automatically reevaluate such configuration parameters and automatically reconfigure the estimation system accordingly. For both types of analyzer, the active set of input measurements for the computerized estimation system can be initially established or periodically updated to perform any variety of configuration tuning operations, including the following: removing linearly redundant variables; removing inputs showing no variation; removing unnecessary or non-estimable inputs; tuning estimation system operating parameters that govern learning rates and the use of recent trends; occasional model accuracy assessment; and tuning monitoring alarm thresholds.
54 Citations
20 Claims
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1. In or for an estimation system operable for receiving input values for successive time trials and, for each time trial, computing output values based on the input values and learned parameters and updating the learned parameters to reflect relationships observed among the input and output values, an analyzer operable for performing the steps of:
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(a) receiving historical data comprising samples of the input and output values for a plurality of time trials;
(b) receiving a set of model reduction configuration parameters including specifications for a statistical model that may be implemented by the estimation system;
(c) activating the estimation system to run the historical data on the statistical model to compute the output values and the learned parameters for the statistical model;
(d) analyzing the learned parameters to identify input values that are ineffective for estimating the output values;
(e) reducing the size of the model by eliminating the ineffective input values. - View Dependent Claims (2, 3, 9)
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4. In or for an estimation system operable for receiving input values for successive time trials and, for each time trial, computing output values based on the input values and learned parameters and updating the learned parameters to reflect relationships observed among the input and output values, an analyzer operable for performing the steps of:
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(a) receiving historical data comprising samples of the input and output values for a plurality of time trials;
(b) receiving a set of candidate model configuration parameters including specifications for a statistical model that may be implemented by the estimation system;
(c) activating the estimation system to run the historical data on the statistical model to compute the output values and learned parameters for the statistical model; and
(d) performing a model assessment by comparing the computed output values to the historical samples of output values. - View Dependent Claims (5, 6, 7, 8)
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10. In or for an estimation system operable for receiving input values for successive time trials and, for each time trial, computing output values based on the input values and learned parameters and updating the learned parameters to reflect relationships observed among the input and output values, an analyzer operable for performing the steps of:
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(a) receiving historical data comprising samples of the input and output values for a plurality of time trials;
(b) receiving a set of model reduction configuration parameters including specifications for a statistical model that may be implemented by the estimation system;
(c) activating the estimation system to run the historical data on the statistical model to compute the output values and the learned parameters for the statistical model;
(d) analyzing the learned parameters to identify input values that are ineffective for estimating the output values;
(e) reducing the size of the model by eliminating the ineffective input values;
(f) repeating steps (b) though (e) to perform a series of model reduction steps comprising basic screening, linear redundancy elimination, and unnecessary input removal to create a reduced size statistical mode;
(g) receiving a set of candidate model configuration parameters including specifications for the reduced size statistical model that may be implemented by the estimation system;
(h) activating the estimation system to run the historical data on the reduced size statistical model to compute the output values and learned parameters for the statistical model; and
(i) performing a model assessment by comparing the computed output values to the historical samples of output values;
(j) repeating steps (g) through (i) for a plurality of candidate model configuration parameters;
(k) identifying a desired set of the configuration parameters based on the plurality of model assessments. - View Dependent Claims (11, 12, 13, 14, 16, 18, 19, 20)
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15. In or for an estimation system operable for receiving input values for successive time trials and, for each time trial, computing output values based on the input values and learned parameters and updating the learned parameters to reflect relationships observed among the input and output values, an analyzer operable for performing the steps of:
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receiving historical data comprising samples of the input and output values for a plurality of time trials;
receiving a set of candidate model configuration parameters including specifications for a statistical model that may be implemented by the estimation system;
activating the estimation system to run the historical data on the statistical model to compute the output values and learned parameters for the statistical model; and
computing alert thresholds for output values based on observed deviance values between the computed output values and the historical samples of output values to obtain a desired alert sensitivity.
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17. In or for an estimation system operable for receiving input values for successive time trials and, for each time trial, computing output values based on the input values and learned parameters and updating the learned parameters to reflect relationships observed among the input and output values, an analyzer operable for performing the steps of:
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continually running several competing models of the estimation system;
occasionally comparing results from the competing models;
based on the comparison;
identifying a best recently performing model; and
generating the output values based on the best recently performing model.
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Specification