Overnight Shibor Forecasting Based On Combination Models

Shibor,the full name is Shanghai Interbank Offered Rate,which is the interest rate that financial peers use to borrow money from each other for financing,it has the characteristics of being openness and marketization.Shibor can reflect the short-term capital supply and demand relationships timely and accurately in the money market and even the entire financial market.It has guiding significance for the control of monetary policies of central banks in various countries.In this paper,the overnight Shibor value is selected as the object of the prediction study,and two combination models are established respectively.One is to do EEMD decomposition of the data and establish BP-Adaboost predictions for the decomposed data;the other is to address the linear limitations of simply using the time series model and the shortcomings of neural network models that are prone to fall into local optimal solutions.The ARIMA-BP-Adaboost model is a predictive model that combines the linear prediction results of the time series model with the nonlinear prediction results of the neural network model.The ARIMA model is used to predict the linear part.The residual is used as the expected output of the nonlinear prediction part.The original data and residual values are trained by BP-Adaboost.The prediction residuals are output,and the residuals of the predicted outputs are summed.The prediction results of the ARIMA model are superimposed,and the prediction results of the ARIMA-BP-Adaboost combination model are obtained.In this paper,we use a variety of models to forecast overnight Shibor data,and finally put forward two combined model prediction methods.By comparing the degree of fit of each model and the prediction error,we find that the prediction accuracy of the combined model is significantly better than simply using Neural network prediction model and time series prediction model.