Hybrid – Regression and Time Series Mixture Approaches to Predict Resilience

March 6, 2024 @ 5:30 pm – 7:30 pm America/New York Timezone
MIT Lincoln Lab Main Cafeteria and Hybrid
244 Wood St
MA 02421

IEEE Boston/Providence/New Hampshire Reliability Chapter and co-sponsoring Life Members

Speaker:  Priscila Silva of University of Massachusetts – Dartmouth

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.

Location:  This Meeting is to be delivered in-person at MIT Lincoln Lab Main Cafeteria, 244 Wood St, Lexington, MA 02421, and virtually.

At registration, you must provide a valid e-mail address to receive the Webinar Session link approximately 15 hours before the event. The link will only be sent to the e-mail address entered with your registration. Please double-check for spelling errors. If you haven’t received the e-mail as scheduled, please check your spam folder and alternate e-mail accounts before contacting the host.


5:30 PM – Light repast and Networking

6:00 PM – Technical Presentation

6:45 PM – Questions and Answers

7:00 PM – Adjournment

The meeting is open to all. You do not need to belong to the IEEE to attend this event; however, we welcome your consideration of IEEE membership as a career enhancing technical affiliation.

There is no cost to register or attend, but registration is required.


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