A Hybrid Forecasting Algorithm and Its Application to Economic Analysis

There exists a great deal of periodic non-stationary processes system in nature, social and economical phenomenon et al. It is very important to realize the dynamic analysis and real-time forecast within a period. In this paper, a wavelet-Kalman hybrid estimation and forecasting algorithm based on step-by-step filtering with the real-time and recursion property is put forward. It combines the advantages of Kalman filter and wavelet transform. Utilizing the information provided by multi-sensor effectively, this algorithm can realize not only realtime tracking and dynamic multi-step forecasting within a period, but also the dynamic forecasting between periods, and has a great value to the system decision-making. Simulation results show that this algorithm is valuable.