Multivariate time series and regression models for forecasting annual maintenance costs of EPDM roofing systems

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The scope of this study focuses on developing and comparing the performances of Vector Autoregession (VAR) time series and multivariate linear regression (MLR) models in predicting the annual maintenance costs of Ethylene Propylene Diene Monomer (EPDM) roofing systems. To accomplish the objective of this study, two prediction models that utilized VAR time series and MLR methodologies were developed to enable a comparison of their performances. These two models were developed in four main phases that focused on data collection, data analysis, model development, and validation. The data collection and analysis phases focused on collecting and analyzing historical data of 16 different EPDM roofing systems for a 23-year period from 1997 to 2019. This data was then used to develop two models for predicting the annual maintenance costs of EPDM roofing systems using VAR and MLR methodologies. The performance of these two models were then compared and analyzed in the validation phase. The findings of this performance analysis indicate that the average accuracy of the stepwise MLR model in predicting the annual maintenance costs of EPDM roofs (85%) was slightly higher than the VAR model (83%). The use of the developed models enables facility managers to improve the accuracy of forecasting future roof annual maintenance costs and to provide a more reliable multi-year plan and budget for their building maintenance programs. • Regression model outperformed time series model in predicting maintenance costs. • Average accuracy of Multivariate Linear Regression model was 85%. • Average accuracy of Vector Autoregression time series model was 83%. • Regression model required less predictor variables than the time series model. • Most significant predictor weather variables were relative humidity and snowfall.