Particle Swarm Optimization-Based Time Series Data Prediction
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.
KeywordsParticle filter Optimization method Sequence data analysis
This research is supported and funded by the National Key Research and Development Plan under Grant No. 2016YFF0202600 and No. 2016YFF0202604, the National Natural Science Foundation of China under Grant No. 71301152 and No. 91646122.
- 4.Ernawati, S.: Using particle swarm optimization to a financial time series prediction. In: International Conference on Distributed Framework and Applications, pp. 1–6 (2010)Google Scholar
- 5.Tian, Z., Wang, P., He, T.: Fuzzy time series based on k-means and particle swarm optimization algorithm. In: Man-Machine-Environment System Engineering (2016)Google Scholar