Performance comparison of artificial intelligence methods for predicting cash flow

Title:

Performance comparison of artificial intelligence methods for predicting cash flow

Creator:

Hongjiu, Liu, Rieg, Robert, and Yanrong, Hu

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:09b0c0bc-620c-4026-b8f1-477cd8a4a7fd
uuid:09b0c0bc-620c-4026-b8f1-477cd8a4a7fd
doi:10.14311/NNW.2012.22.034

Subject:

Performance, prediction, and cash flow

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Cash flow forecasting is indispensable for managers, investors and banks. However, which method is more robust has been argued under the condition of small size samples. With sliding window technique we create the Response Surface, Back Propagation Neural Network, Radial Basis Functions Neural Network and Support Vector Machine models respectively, which are examined by comparing performances of training and simulation. Performances of training models are measured by mean of squared errors while that of simulation is done by average relative errors of the results. By comparison, Support Vector Machine is most robust to forecast cash flow, followed by Radial Basis Function Neural Network, the third Back Propagation Neural Network and the last Response Surface Model. The optimal result of each model depends on the window size of the transmitter.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2012 Volume:22 | Number:6

Harvested from:

CDK

Metadata only:

false