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Original Articles

Parameter prediction for cash flow forecasting models

Pages 397-413 | Published online: 28 Jul 2006

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Abdelazeem S. Abdelazeem & Ahmed H. Ibrahim. (2022) Evaluation of project cost and schedule performance using fuzzy theory-based polynomial function. International Journal of Construction Management 22:13, pages 2564-2576.
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Dubem I. Ikediashi & Kevin C. Okolie. (2022) An assessment of risks associated with contractor’s cash flow projections in South-South, Nigeria. International Journal of Construction Management 22:11, pages 2051-2058.
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Min-Chun Yu, Van-Loi Dang & Hui-Chung Yeh. (2017) Measuring cash flow and overdraft for fuzzy project networks with overlapping activities. Journal of Civil Engineering and Management 23:4, pages 487-498.
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Andrew Ross, Katie Dalton & Begum Sertyesilisik. (2013) An investigation on the improvement of construction expenditure forecasting. Journal of Civil Engineering and Management 19:5, pages 759-771.
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Henry A. Odeyinka, John Lowe & Ammar P. Kaka. (2013) Artificial neural network cost flow risk assessment model. Construction Management and Economics 31:5, pages 423-439.
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FrancoK. T. Cheung & Martin Skitmore. (2006) A modified storey enclosure model. Construction Management and Economics 24:4, pages 391-405.
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J. Nicholas & D. J. Edwards. (2003) A model to evaluate materials suppliers' and contractors' business interactions. Construction Management and Economics 21:3, pages 237-245.
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RUSSELL KENLEY. (1999) Cash farming in building and construction: a stochastic analysis. Construction Management and Economics 17:3, pages 393-401.
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Articles from other publishers (12)

Dong Hoon Kwak, Young In Cho, Sung Won Choe, Hyun Joo Kwon & Jong Hun Woo. (2022) Optimization of long-term planning with a constraint satisfaction problem algorithm with a machine learning. International Journal of Naval Architecture and Ocean Engineering 14, pages 100442.
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Juan Pedro Ruiz-Fernández, Javier Benlloch, Miguel A. López & Nelia Valverde-Gascueña. (2019) Influence of seasonal factors in the earned value of construction. Applied Mathematics and Nonlinear Sciences 4:1, pages 21-34.
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Pradip K. Bhaumik. (2016) Developing and Using a New Family of Project S-Curves Using Early and Late Shape Parameters. Journal of Construction Engineering and Management 142:12.
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Weisheng Lu, Yi Peng, Xi Chen, Martin Skitmore & Xiaoling Zhang. (2016) The S-curve for forecasting waste generation in construction projects. Waste Management 56, pages 23-34.
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Li-Chung Chao & Hsien-Tse Chen. (2015) Predicting project progress via estimation of S-curve's key geometric feature values. Automation in Construction 57, pages 33-41.
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F. Valderrama & R. Guadalupe. 2015. Project Management and Engineering. Project Management and Engineering 45 60 .
Rattachut Tangsucheeva & Vittaldas Prabhu. (2014) Stochastic financial analytics for cash flow forecasting. International Journal of Production Economics 158, pages 65-76.
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M. Chiao Lin, H. Ping Tserng, S. Ping Ho & D.L. Young. (2012) A novel dynamic progress forecasting approach for construction projects. Expert Systems with Applications 39:3, pages 2247-2255.
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Li-Chung Chao & Ching-Fa Chien. (2010) A Model for Updating Project S-curve by Using Neural Networks and Matching Progress. Automation in Construction 19:1, pages 84-91.
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Li-Chung ChaoChing-Fa Chien. (2009) Estimating Project S-Curves Using Polynomial Function and Neural Networks. Journal of Construction Engineering and Management 135:3, pages 169-177.
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Franco K.T. Cheung & Martin Skitmore. (2006) Application of cross validation techniques for modelling construction costs during the very early design stage. Building and Environment 41:12, pages 1973-1990.
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Martin Skitmore. (1998) A method for forecasting owner monthly construction project expenditure flow. International Journal of Forecasting 14:1, pages 17-34.
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