Prediction of Displacement Time Series Based on Support Vector Machines-Markov Chain

Abstract

A new displacement time series predicting model was proposed by combining the Support Vector Machines and the Markov Chain, which was named as Support Vector Machines and Markov Chain (SVM-MC) model. Through studying the measured displacement, SVM optimized by particle swarm optimization (PSO) was used to forecast the trend of macro development in roll. Markov chain was applied to compute State Transition Probability Matrix. By classifying system state and calculating absolute error and relative error between measured value and SVM fitting value, the predicting results are improved. The model was used on predicting displacement time series of a high slope of a permanent lock. The engineering case studies indicated that the model was scientific and reliable, and there was engineering practical value for displacement time series forecasting.