Forecasting costs in construction projects has an important place in whole processes. It is very important to observe possible changes in the construction process, especially in large scale projects. Using a series of data in a functional way can be useful to make the changes that can be made at a later time in that field. Grey models have an important place in the time series forecasting model. In line with this case, there are a number of methods developed over time. In fact, when these models are examined, it is seen that they follow and complement each other in the developmental stages, but the levels of development differ with some important decompositions. The ability to model and forecast construction costs can result in more accurate cost forecasting and budgeting. This has been modeled using a residual Fourier model by analyzing the remains of the Grey Verhulst Model, conditional variability of construction cost prices. The results show that the developed model can forecast construction costs with less errors. It was observed that the deviation rate in the final values decreased up to 0.97%. The data used in the time series analysis were observed for a relatively long time. These data are calculated with the assumption that the system will be developed in the future and the forecasted values will be maintained in the future based on these time series data.