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

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書目名稱Construction Analytics影響因子(影響力)




書目名稱Construction Analytics影響因子(影響力)學(xué)科排名




書目名稱Construction Analytics網(wǎng)絡(luò)公開度




書目名稱Construction Analytics網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Construction Analytics被引頻次




書目名稱Construction Analytics被引頻次學(xué)科排名




書目名稱Construction Analytics年度引用




書目名稱Construction Analytics年度引用學(xué)科排名




書目名稱Construction Analytics讀者反饋




書目名稱Construction Analytics讀者反饋學(xué)科排名

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Mohsen Shahandashti,Bahram Abediniangerabi,Sooin KIllustrates theoretical explanations of construction analytics, hands-on practices, and R codes for analytics techniques.Enables readers to investigate the problems in the construction industry such a

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https://doi.org/10.1007/978-3-322-88550-0nhance 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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https://doi.org/10.1007/978-3-322-88557-9struction time series data have not shown a constant variance. The volatility of a construction time series variable over time is challenging for accurate forecasting and risk management. This chapter discusses two time series volatility models (i.e., ARCH and GARCH) to forecast the variance of a co

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https://doi.org/10.1007/978-3-322-88557-9ls. 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

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