Auto regressive moving average (ARMA) prediction method of bank cash flow time series

In order to improve the accuracy and real-time of all kinds of information in the cash business, enhance the linkage between cash inventory forecasting and cash management information in the commercial bank, and make a correct decision on avoiding financial risks for bank, the auto regressive moving average (ARMA) model is adopted to realize the time series prediction of bank cash flow. Since most of the economic time series are non-stationary, so through the differential treatment of the original sequence, which is converted into stationary time series (pre-treatment), the ARMA model is built and the moment estimation method is used to estimate model parameters. Finally, the forecasting results are re-modified, which improves the accuracy of prediction. The simulation comparison experiments are carried out with the moving average prediction method on the reality commercial bank´s cash flow data and the predictive performance comparison results show the effectiveness of the proposed methods.