Simulation of Time Series Prediction Based on Hybrid Support Vector Regression

The paper proposes a hybrid methodology that exploits the unique strength of the autoregressive integrated moving average model and the support vector machine model in forecasting time series. The simulation experiment results showed that the hybrid model is superior to the individual models for the test values of the turbo-generator vibration. Most of the individual models evaluated showed poor ability to detect directional change. This problem can be overcome with the use of the hybrid model. Besides superior turning point detectability, the hybrid model could achieve superior predictive performance and showed promising results. Therefore, the results suggested that the proposed hybrid model is typically a reliable forecasting tool for application within the forecasting fields of time series.