Abstract:

For complex application environments, it is difficult to get accurate time series modeling and multi-step prediction results. Multi-step prediction of rolling time series based on the particle filter optimization (PF_RTS) is proposed to solve the problem. According to the modeling thoughts of Box-Jenkins, the time series is adaptively modeled. And the model is regarded as the particles’ state transition equation. By using a particle filtering algorithm, the optimal state is estimated and the predicted data are real-time corrected. With self-learning ability, this algorithm is suitable for realtime applications. The simulation results show that this method needs less prior knowledge and has a better predictive accuracy.