US 7,127,439 B2 · Automated analyzers for estimation systems

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.

Claims25

Dependent claims

A method for operating an analyzer for an estimation system operable for receiving input values for successive time trials, 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, comprising 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 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;
and
(f) periodically repeating steps (a) though (e) to reinstate into the model previously eliminated input values that have become effective for predicting the output values.

4 more elements

receiving historical data comprising samples of the input and output values for a plurality of time trials;
receiving a plurality of sets of candidate model configuration parameters, each set including specifications for an intrinsically different statistical model that may be implemented by the estimation system;
activating the estimation system to run the historical data on each statistical model to compute the output values and learned parameters for each statistical model;
analyzing the learned parameters to identify input values that are ineffective for estimating the output values;
reducing the size of the model by eliminating the ineffective input values;
performing a qualitative model assessment for each statistical model;
selecting a best-performing statistical model for the estimation system based on the plurality of qualitative model assessments;
and
implementing the best-performing statistical model with the estimation system to compute output values for future time trials.

6 more elements

Dependencies are inferred from the claim text.

Automated analyzers for estimation systems

Life of this patent

Automated analyzers for estimation systems

Life of this patent

Abstract

Claims25