Abstract
Identifying leading indicators of construction cost time series and using them as explanatory variables could improve the accuracy of forecasting models. This chapter explains the process of identifying the leading indicators of a construction time series and developing proper multivariate models, such as vector error correction and vector autoregressive models for forecasting them. Several practical examples are provided along with R codes to show how to create and diagnose multivariate time series models for forecasting construction variables. A multivariate time series model is developed for forecasting monthly Highway Construction Spending (HCS) time series using consumer price index (CPI) as the leading indicator, and its performance is compared with the results of the univariate seasonal ARIMA model. The comparison results show that the VEC model outperforms the seasonal autoregressive integrated moving average (SARIMA) model based on typical error measures.
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Shahandashti, M., Abediniangerabi, B., Zahed, E., Kim, S. (2023). Construction Time Series Forecasting Using Multivariate Time Series Models. In: Construction Analytics. Springer, Cham. https://doi.org/10.1007/978-3-031-27292-9_4
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DOI: https://doi.org/10.1007/978-3-031-27292-9_4
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