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Predicting Construction Costs under Uncertain Market Conditions: Probabilistic Forecasting Using Autoregressive Recurrent Networks Based on DeepAR
Ghiwa Assaf
,
Rayan H. Assaad
, Islam H. El-Adaway
, Mohamad Abdul Nabi
Civil and Environmental Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
2
Scopus citations
Overview
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Dive into the research topics of 'Predicting Construction Costs under Uncertain Market Conditions: Probabilistic Forecasting Using Autoregressive Recurrent Networks Based on DeepAR'. Together they form a unique fingerprint.
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Keyphrases
Network Applications
100%
Construction Cost
100%
Market Conditions
100%
Recurrent Network
100%
Uncertain Market
100%
Probabilistic Forecasting
100%
DeepAR
100%
Construction Materials
75%
Stochastic Model
50%
Cost Estimation
50%
Post-COVID-19
50%
Construction Material Cost
50%
Innovative Approach
25%
COVID-19
25%
Training Set
25%
Market Value
25%
Cost Overrun
25%
Monte Carlo Sampling
25%
Steel Material
25%
Mean Absolute Percentage Error
25%
Prediction Interval
25%
Price Data
25%
Concrete Products
25%
Material Prices
25%
Price Escalation
25%
Market Uncertainty
25%
First-price
25%
Quantile Estimates
25%
Iron-based Materials
25%
Probabilistic Assessment
25%
Point Estimation
25%
Interval Assessment
25%
Engineering
Construction Cost
100%
Stochastic Model
100%
Recurrent Network
100%
Material Cost
100%
Cost Estimate
50%
Cost Estimation
50%
Quantile
50%