Purdue Index for Construction Analytics: Prediction and Forecasting Model Development
Arkaprabha Bhattacharyya, Soojin Yoon, Theodore J. Weidner, Makarand Hastak
Abstract
The Purdue Index for Construction (Pi-C) was developed to gauge the health of the construction industry. It is a composite index consisting of five dimensions: economic, stability, social, development, and quality. This research conducts a data-driven analysis to provide prediction and time-series forecasting models for Pi-C to (1) monitor; and (2) provide guidance on how to improve the future health trajectory for the US construction industry. The seasonal autoregressive integrated moving average (SARIMA) technique is applied for the future trend analysis; multiple linear regression (MLR) and random forests (RF) are applied for prediction models of Pi-C data analytics. It is expected that the proposed prediction and time-series forecasting models will help decision-makers, including policy developers and construction practitioners, to take necessary action in a timely manner, as well as open the discourse on the advanced application of analytics and data-driven decision-making in the construction industry.