Multivariable analysis, correlation, and prediction

Misty Blowers; Jose Iribarne; Gary Scott

doi:10.1117/12.821899

28 April 2009 Multivariable analysis, correlation, and prediction

Misty Blowers, Jose Iribarne, Gary Scott

Proceedings Volume 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III; 734702 (2009) https://doi.org/10.1117/12.821899
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

Making best use of multi-point observations and sensor information to forecast future events in complex real time systems is a challenge which presents itself in many military and industrial problem domains. The first step in tackling these challenges is to analyze and understand the data. Depending on the algorithm used to forecast a future event, improvements to a prediction can be realized if one can first determine the nature and extent of variable correlations, and for the purposes of prediction, quantify the strength of the correlations of input variables to output variables. This is no easy task since sensor readings and operator logs are sometimes inconsistent and/or unreliable, some catastrophic failures can be almost impossible to predict, and time lags and leads in a given system may vary from one day to the next. Correlation analysis techniques can help us deal with some of these problems. They allow us to find out what variables may be strongly correlated to major events. After detecting where the strongest correlations exist, one must choose a model which can best predict the possible outcomes that could occur for a number of possible scenarios. The model must be tested and evaluated, and sometimes it is necessary to go back to the feature selection stage of the model design process and reevaluate the available sensory data and inputs. An industrial process example is adopted in this research to both highlight the issues that arise in complex systems and to demonstrate methods of addressing such issues.

Citation Download Citation

Misty Blowers, Jose Iribarne, and Gary Scott "Multivariable analysis, correlation, and prediction", Proc. SPIE 7347, Evolutionary and Bio-Inspired Computation: Theory and Applications III, 734702 (28 April 2009); https://doi.org/10.1117/12.821899

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