Integrated Time Series Forecasting approaches using moving average, grey prediction, support vector regression and bagging for NNGC

Time series prediction is an interesting and challenging task in the field of data mining. This paper focuses on the monthly time series in NNGC. There are two main kinds of approaches, i.e. statistical approaches and computational intelligence approaches, which deal with time series prediction. We treat moving average and grey prediction from the statistical field as our benchmarks. We then combine these two approaches respectively with support vector regression (SVR) from the computational intelligence field. The hybrid SVR approaches outperform moving average and grey prediction based on the criteria of MAPE, SMAPE and RMSE. Finally, we further integrate these hybrid SVR approaches with the technique of the bagging ensembles to further achieve a better performance.