Cash flow information is crucial for the decision making process in construction management. Due to the complexity and the dynamic progress of a construction project, forecasting cash flow demand throughout various phases of the project remains a challenging problem. This article presents a novel inference model, named as Adaptive Timedependent Least Squares Support Vector Machine (LS-SVMAT) for cash flow prediction. In the LS-SVMAT, Least Squares Support Vector Machine (LS-SVM) is integrated with an adaptive time function (ATF) to generalize the inputoutput mapping of cash flow. Since cash flow data are time-dependent, data points recorded in different periods can contribute dissimilarly to the training process of the prediction model. Thus, the role of the ATF is to determine the appropriate weight associated with each data point at a specific time period. By doing so, LS-SVMAT can better deal with the dynamic nature of the time series. Furthermore, to identify the optimal parameters for the inference model, Differential Evolution (DE) based cross validation process is utilized in this research. Comparing to other benchmark methods, the proposed model has identified the most appropriate time function and has yielded superior forecasting results. Therefore, LS-SVMAT can be a promising tool for construction managers in cash flow prediction.