Abstract
Resilience engineering is the ability to design, build, and sustain systems that can deal effectively with disruptive events. Previous research focused on resilience models that were not designed to predict multiple disruptions and recoveries, and resilience metrics, which are typically calculated after disruptions. Therefore, this article introduces a new approach combining regression and time series methods to track and predict system performance under multiple shocks, offering a framework for planning resilience tests and guiding data collection applicable to various systems and processes. To illustrate, subsets ranging from 50% to 80% of a historical job loss dataset from the 1980 U.S. recession were used for model fitting to assess generalization and stability. Goodness-of-fit measures, confidence intervals, and resilience metrics validated this approach against established statistical methods and a neural network model. The results indicate that traditional statistical models fail to capture minor changes when fitted with small datasets, and neural networks are overly sensitive to the size of the training data. In contrast, the novel mixture approach considering immediate and delayed disruptions exhibits superior long-term predictive performance and greater accuracy in forecasting resilience metrics, even when only 50% of the data is used for model fitting.