Cash flow prediction of a bank deposit using scalable graph analysis and machine learning

2021 ◽  
Author(s):  
Ryo Kawahara ◽  
Mikio Takeuchi
Keyword(s):  
Machine Learning ◽  
Cash Flow ◽  
Graph Analysis ◽  
Flow Prediction

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emmanuel Adinyira ◽  
Emmanuel Akoi-Gyebi Adjei ◽  
Kofi Agyekum ◽  
Frank Desmond Kofi Fugar
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cash Flow ◽  
Predictive Accuracy ◽  
Model Development ◽  
Construction Project ◽  
Support Vector ◽  
Sensitivity Index ◽  
Content Type ◽  
Hyperparameter Selection

PurposeKnowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.Design/methodology/approachThe study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.FindingsThe developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.Originality/valueThe developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

scholarly journals Usefulness of fair valuation of biological assets for cash flow prediction

2017 ◽  
Vol 47 (2) ◽  
pp. 157-180 ◽  
Author(s):  
Josep Maria Argilés-Bosch ◽  
Meritxell Miarons ◽  
Josep Garcia-Blandon ◽  
Carmen Benavente ◽  
Diego Ravenda
Keyword(s):  
Cash Flow ◽  
Flow Prediction ◽  
Fair Valuation

Big data‐driven machine learning‐enabled traffic flow prediction

2018 ◽  
Vol 30 (9) ◽  
Author(s):  
Fanhui Kong ◽  
Jian Li ◽  
Bin Jiang ◽  
Tianyuan Zhang ◽  
Houbing Song
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Traffic Flow ◽  
Data Driven ◽  
Traffic Flow Prediction ◽  
Flow Prediction
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