Systems Engineering and Electronics

• SOFTWARE ALGORITHM AND SIMULATION • Previous Articles     Next Articles

Integrated parallel forecasting model based on modified fuzzy time series and SVM

Yong Shuai1,2,*, Tailiang Song3, and Jianping Wang1   

  1. 1. Technical Support Engineering Faculty, Academy of Armored Forced Engineering, Beijing 100072, China;
    2. Unit 68207 of the PLA, Jiayuguan 735100, China;
    3. China Defense Science & Technology Information Center, Beijing 100142, China
  • Online:2017-08-25 Published:2010-01-03


A dynamic parallel forecasting model is proposed, which is based on the problem of current forecasting models and their combined model. According to the process of the model, the fuzzy C-means clustering algorithm is improved in outliers operation and distance in the clusters and among the clusters. Firstly, the input data sets are optimized and their coherence is ensured,the region scale algorithm is modified and non-isometric multiscale region fuzzy time series model is built. At the same time, the particle swarm optimization algorithm about the particle speed, location and inertia weight value is improved, this method is used to optimize the parameters of support vector machine, construct the combined forecast model, build the dynamic parallel forecast model, and calculate the dynamic weight values and regard the product of the weight value and forecast value to be the final forecast values. At last, the example shows the improved forecast model is effective and accurate.