Research on Intelligent Analysis and Prediction Model of Funds Flow Based on Non-stationary Time Series

Abstract

Abstract Funds flow prediction is critical to the stable operation of financial service institutions. In view of the non-stationary characteristics of funds inflow and outflow in financial institutions, this paper introduces the key characteristics of time series analysis, compares AR, MA, ARMA and ARIMA models, and proposes a funds flow prediction model based on ARIMA algorithm. The stationary test, pure randomness test and model order determination of time series are explained in detail. Finally, combined with the error and score of the model, the relatively optimal model is selected to predict the test data. The experimental results show that, for non-stationary time series, the model has smaller prediction error and can effectively predict funds flow.