Particle Swarm Optimization-Based Time Series Data Prediction

Abstract

Time series data is one of the forms of product quality inspection data. It is significant for analyzing and processing big data of product quality inspection to research prediction method of time series data. In this paper, we focus on the problem of the existence of particle scattering and the problem of the lack of computational efficiency. Particle swarm optimization (PSO) is integrated into the standard particle filter algorithm, which improves the sampling process of the particle and optimizes the distribution of the sample, and accelerates the convergence of the particle set. Speed, and improve the performance of particle filter. On this basis, the similarity between particle filter and artificial fish swarm algorithm is analyzed. Based on this similarity, the foraging behavior and clustering behavior of artificial fish. The results show that the proposed algorithm can effectively analyze the time series data. The results show that the proposed algorithm can be used to analyze the residual life prediction of particle swarm optimization based on artificial particle swarm optimization.