Forecasting daily cash turnover of bank with EWMA and SVR

Abstract

This paper present one forecasting method with Exponential Weighted Moving Average(EWMA) and Support Vector Regression (SVR). The daily cash turnover of the banks is time-serial data, banks need to forecasting daily cash turnover for banking reserve. First, the time series is preprocessed with EWMA method. The EWMAs with different coefficients are selected for forecasting features. And then SVR is used in the transformed dataset with EWMA for forecasting. The experimental result shows that the EWMA can improve the forecasting accuracy, and the SVR is more effective than other method such as 1-NN and MLP. Statistical correlation of SVR between the forecasted and actual values is much higher than other method such as 1-NN and MLP. © 2010 Springer-Verlag Berlin Heidelberg.