Proper cash flow management demands the needs of a reliable method to forecast future cash flow. Researchers attempted to forecast the cash flow based on the previous period of cash flow, which is a time series problem, without any consideration of the characteristic of the project. Since the project diverse one another, the cash flow of the construction projects may be different. To counter this problem, Neural Network-Long Short-Term Memory (NN-LSTM) covers this time series problem by considering the complexity of the project. However, the difficulties of finding the optimal parameters of the network restrain the performance of the model. This work proposed the advancement of the model, named Symbiotic Organisms Search Neural Network-Long Short-Term Memory (SOS NN-LSTM), which utilizing Symbiotic Organisms Search (SOS) algorithm to obtain the optimal architecture of the network and train the connections attached simultaneously. Our experimental results demonstrated a promising prediction performance on forecasting the cash flow in the construction project.

Proper cash flow management demands the needs of a reliable method to forecast future cash flow. Researchers attempted to forecast the cash flow based on the previous period of cash flow, which is a time series problem, without any consideration of the characteristic of the project. Since the project diverse one another, the cash flow of the construction projects may be different. To counter this problem, Neural Network-Long Short-Term Memory (NN-LSTM) covers this time series problem by considering the complexity of the project. However, the difficulties of finding the optimal parameters of the network restrain the performance of the model. This work proposed the advancement of the model, named Symbiotic Organisms Search Neural Network-Long Short-Term Memory (SOS NN-LSTM), which utilizing Symbiotic Organisms Search (SOS) algorithm to obtain the optimal architecture of the network and train the connections attached simultaneously. Our experimental results demonstrated a promising prediction performance on forecasting the cash flow in the construction project.

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