How to cite this paper
Amalnik, M., Iranmanesh, H., Asghari, A., Mollajan, A., Fadakar, V & Daneshazarian, R. (2019). Cash flow prediction using artificial neural network and GA-EDA optimization.Journal of Project Management, 4(1), 43-56.
Refrences
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Andrawis, R. R., Atiya, A. F., & El-Shishiny, H. (2011). Forecast combinations of computational in-telligence and linear models for the NN5 time series forecasting competition. International Journal of Forecasting, 27(3), 672-688.
Bec, F., & Mogliani, M. (2015). Nowcasting French GDP in real-time with surveys and “blocked” re-gressions: Combining forecasts or pooling information? International Journal of Forecasting, 31(4), 1021-1042.
Blanc, S. M., & Setzer, T. (2015). Analytical debiasing of corporate cash flow forecasts. European Journal of Operational Research, 243(3), 1004-1015.
Chen, H. L., O’Brien, W. J., & Herbsman, Z. J. (2005). Assessing the accuracy of cash flow models: the significance of payment conditions. Journal of Construction Engineering and Management, 131(6), 669-676.
Cheng, M. Y., & Roy, A. F. (2011). Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. International Journal of Project Management, 29(1), 56-65.
Cheng, M. Y., Tsai, H. C., & Liu, C. L. (2009). Artificial intelligence approaches to achieve strategic control over project cash flows. Automation in Construction, 18(4), 386-393.
Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2010). Evolutionary fuzzy hybrid neural network for pro-ject cash flow control. Engineering Applications of Artificial Intelligence, 23(4), 604-613.
Ghysels, E., & Ozkan, N. (2015). Real-time forecasting of the US federal government budget: A sim-ple mixed frequency data regression approach. International Journal of Forecasting, 31(4), 1009-1020.
Holland, J. H., & Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learn-ing. Massachusetts: Addison-Wesley.
Hwee, N. G., & Tiong, R. L. (2002). Model on cash flow forecasting and risk analysis for contracting firms. International Journal of Project Management, 20(5), 351-363.
Kao, L. J., Chiu, C. C., Lu, C. J., & Yang, J. L. (2013). Integration of nonlinear independent compo-nent analysis and support vector regression for stock price forecasting. Neurocomputing, 99, 534-542.
Khosrowshahi, F., & Kaka, A. P. (2007). A decision support model for construction cash flow man-agement. Computer‐Aided Civil and Infrastructure Engineering, 22(7), 527-539.
Larrañaga, P., & Lozano, J. A. (Eds.). (2001). Estimation of distribution algorithms: A new tool for evolutionary computation (Vol. 2). Springer Science & Business Media.
Li, Y., Moutinho, L., Opong, K. K., & Pang, Y. (2015). Cash flow forecast for South African firms. Review of Development Finance, 5(1), 24-33.
Mühlenbein, H. (1997). The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3), 303-346.
Namazi, M., Shokrolahi, A., & Maharluie, M. S. (2016). Detecting and ranking cash flow risk factors via artificial neural networks technique. Journal of Business Research, 69(5), 1801-1806.
Russell, J. S. (1991). Contractor failure: analysis. Journal of Performance of Constructed Facilities, 5(3), 163-180.
Son, H., Kim, C., & Kim, C. (2012). Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project plan-ning variables. Automation in Construction, 27, 60-66.
Touran, A., Atgun, M., & Bhurisith, I. (2004). Analysis of the United States Department of Transpor-tation prompt pay provisions. Journal of Construction Engineering and Management, 130(5), 719-725.
Venkatesh, K., Ravi, V., Prinzie, A., & Van den Poel, D. (2014). Cash demand forecasting in ATMs by clustering and neural networks. European Journal of Operational Research, 232(2), 383-392.
Xiong, T., Bao, Y., & Hu, Z. (2013). Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Economics, 40, 405-415.
Andrawis, R. R., Atiya, A. F., & El-Shishiny, H. (2011). Forecast combinations of computational in-telligence and linear models for the NN5 time series forecasting competition. International Journal of Forecasting, 27(3), 672-688.
Bec, F., & Mogliani, M. (2015). Nowcasting French GDP in real-time with surveys and “blocked” re-gressions: Combining forecasts or pooling information? International Journal of Forecasting, 31(4), 1021-1042.
Blanc, S. M., & Setzer, T. (2015). Analytical debiasing of corporate cash flow forecasts. European Journal of Operational Research, 243(3), 1004-1015.
Chen, H. L., O’Brien, W. J., & Herbsman, Z. J. (2005). Assessing the accuracy of cash flow models: the significance of payment conditions. Journal of Construction Engineering and Management, 131(6), 669-676.
Cheng, M. Y., & Roy, A. F. (2011). Evolutionary fuzzy decision model for cash flow prediction using time-dependent support vector machines. International Journal of Project Management, 29(1), 56-65.
Cheng, M. Y., Tsai, H. C., & Liu, C. L. (2009). Artificial intelligence approaches to achieve strategic control over project cash flows. Automation in Construction, 18(4), 386-393.
Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2010). Evolutionary fuzzy hybrid neural network for pro-ject cash flow control. Engineering Applications of Artificial Intelligence, 23(4), 604-613.
Ghysels, E., & Ozkan, N. (2015). Real-time forecasting of the US federal government budget: A sim-ple mixed frequency data regression approach. International Journal of Forecasting, 31(4), 1009-1020.
Holland, J. H., & Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learn-ing. Massachusetts: Addison-Wesley.
Hwee, N. G., & Tiong, R. L. (2002). Model on cash flow forecasting and risk analysis for contracting firms. International Journal of Project Management, 20(5), 351-363.
Kao, L. J., Chiu, C. C., Lu, C. J., & Yang, J. L. (2013). Integration of nonlinear independent compo-nent analysis and support vector regression for stock price forecasting. Neurocomputing, 99, 534-542.
Khosrowshahi, F., & Kaka, A. P. (2007). A decision support model for construction cash flow man-agement. Computer‐Aided Civil and Infrastructure Engineering, 22(7), 527-539.
Larrañaga, P., & Lozano, J. A. (Eds.). (2001). Estimation of distribution algorithms: A new tool for evolutionary computation (Vol. 2). Springer Science & Business Media.
Li, Y., Moutinho, L., Opong, K. K., & Pang, Y. (2015). Cash flow forecast for South African firms. Review of Development Finance, 5(1), 24-33.
Mühlenbein, H. (1997). The equation for response to selection and its use for prediction. Evolutionary Computation, 5(3), 303-346.
Namazi, M., Shokrolahi, A., & Maharluie, M. S. (2016). Detecting and ranking cash flow risk factors via artificial neural networks technique. Journal of Business Research, 69(5), 1801-1806.
Russell, J. S. (1991). Contractor failure: analysis. Journal of Performance of Constructed Facilities, 5(3), 163-180.
Son, H., Kim, C., & Kim, C. (2012). Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project plan-ning variables. Automation in Construction, 27, 60-66.
Touran, A., Atgun, M., & Bhurisith, I. (2004). Analysis of the United States Department of Transpor-tation prompt pay provisions. Journal of Construction Engineering and Management, 130(5), 719-725.
Venkatesh, K., Ravi, V., Prinzie, A., & Van den Poel, D. (2014). Cash demand forecasting in ATMs by clustering and neural networks. European Journal of Operational Research, 232(2), 383-392.
Xiong, T., Bao, Y., & Hu, Z. (2013). Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices. Energy Economics, 40, 405-415.