Titlebook: Construction Analytics; Forecasting and Inve Mohsen Shahandashti,Bahram Abediniangerabi,Sooin K Textbook 2023 The Editor(s) (if applicable)

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Construction Forecasting Using Time Series Volatility Models,rform the ARIMA model assuming a constant variance in terms of accuracy. R code examples are provided to develop time series volatility models and forecast a time series considering its time-varying variance. Exercise problems are presented at the end of the chapter for readers to review and practic

盡管

Investment Valuation of Construction Projects Under Uncertainty,e-cycle cost analysis method, and determines the probability distribution of the life-cycle cost. Real options analysis evaluates real (nonfinancial) investments under uncertainty with elements for strategic management flexibility and delayed investment. Various examples of construction investment v

Fantasy

https://doi.org/10.1007/978-3-322-88550-0engineers in identifying the appropriate time to invest, quantifying the investment risks in projects, and determining the optimum value of an investment for maximizing the returns on investments. This book provides theoretical explanations, hands-on practice problems with R code scripts, and exerci

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Charakteristika des Baumarktes,e-cycle cost analysis method, and determines the probability distribution of the life-cycle cost. Real options analysis evaluates real (nonfinancial) investments under uncertainty with elements for strategic management flexibility and delayed investment. Various examples of construction investment v

口訣

vide the necessary functions for performing investment valuation. The book maximizes students’ understanding of the necessary theoretical background of data analytics, and explains the implementation of data analytics techniques to solve the actual problems in the construction industry.?.?..978-3-031-27294-3978-3-031-27292-9

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Introduction to Construction Analytics,nhance construction productivity, and reduce construction cost overruns. Although data analytics have tremendous potential to improve strategic decision-making in the construction industry as an ever-increasing volume of data becomes available, it has not been fully exploited on a larger scale in th

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Construction Time Series Forecasting Using Univariate Time Series Models,ojects. This chapter aims to introduce several construction time series variables, such as Highway Construction Spending and National Highway Construction Cost Index, and demonstrate procedures for investigating the characteristics of such time series. This chapter introduces several univariate time

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Hemiparesis

Construction Time Series Forecasting Using Multivariate Time Series Models,ls. 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 sh