Hybrid – Regression and Time Series Mixture Approaches to Predict Resilience

Loading Events

Resilience engineering is the ability to build and sustain a system that can deal effectively with disruptive events. Previous resilience engineering research focuses on metrics to quantify resilience and models to characterize system performance. However, resilience metrics are normally computed after disruptions have occurred and existing models lack the ability to predict one or more shocks and subsequent recoveries.
To address these limitations, this talk presents three alternative approaches to model system resilience with statistical techniques based on (i) regression, (ii) time series, and (iii) a combination of regression and time series to track and predict how system performance will change when exposed to multiple shocks and stresses of different intensity and duration, provide structure for planning tests to assess system resilience against particular shocks and stresses and guide data collection necessary to conduct tests effectively.
These modeling approaches are general and can be applied to systems and processes in multiple domains. A historical data set on job losses during the 1980 recessions in the United States is used to assess the predictive accuracy of these approaches. Goodness-of-fit measures and confidence intervals are computed and interval-based and point-based resilience metrics are predicted to assess how well the models perform on the data set considered. The results suggest that resilience models based on statistical methods such as multiple linear regression and multivariate time series models are capable of modeling and predicting resilience curves exhibiting multiple shocks and subsequent recoveries. However, models that combine regression and time series account for changes in performance due to current and time-delayed effects from disruptions most effectively, demonstrating superior performance in long-term predictions and higher goodness-of-fit despite increased parametric complexity.
Co-sponsored by: Boston Section Life Member Affinity Group
Speaker(s): Priscila Silva,
5:30 PM Light repast and Networking
6:00 PM Technical Presentation
6:45 PM Questions and Answers
7:00 PM Adjournment
Bldg: Main Cafeteria, Lincoln Laboratory, 244 Wood St, Lexington, Massachusetts, United States, 02421, Virtual: https://events.vtools.ieee.org/m/406423

Share This Story, Choose Your Platform!

Go to Top