PSO-Markov residual correction method based on Verhulst-Fourier prediction model

• Main Authors: Tong Niu, Lin Zhang, Bo Zhang, Bo Li, Baoshan Zhang, Wenfeng Wang
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Systems Science & Control Engineering
• Subjects: residual correction; national defense expenditure prediction; verhulst-fourier model; markov model; pso algorithm
• Online Access: http://dx.doi.org/10.1080/21642583.2020.1863277

Macroeconomic predicting is a research hotspot in the field of predicting. The accuracy of predicting often directly affects the rationality of decision-making, especially for defense expenditure predicting. This paper studies the residual correction method of prediction model based on time series. Firstly, based on the grey nonlinear Verhulst prediction model, Fourier series is introduced in this paper to correct the residual sequence once and establish a residual correction model. On this basis, this paper also introduces Markov related concepts, creatively introduces the two-dimensional residual data into Markov state transition matrix, classifies it by K-means clustering analysis, and calculates its parameters by PSO algorithm to realize the secondary accurate correction of residual. Finally, a PSO-Markov residual correction method based on Verhulst-Fourier model is proposed. Tested by examples, this method effectively improves the prediction accuracy of the model, and the prediction is more reliable and accurate.