A Combining Forecasting Method Based on Seasonal Unit Root Test and Support Vector Regression

For catching and processing seasonality in seasonal time series, a combining forecastingmethod based on seasonal unit root test, i.e., the Dickey-Hasza-Fuller (DHF) test and support vector regression (SVR) is proposed, which is denoted as DHF-SVR method. The DHF-SVR method employs DHF test to identify seasonality in series and utilizes seasonal differencing operator to process the seasonal time series; for solving the difficulty of adaptive selection of maximum lag order, a SVR hyper-parameters tuning method based on a genetic algorithm (GA) with real-integer hybrid encoding is proposed. The experimental comparison demonstrates that the proposed DHF-SVR method could improve the forecasting performance in comparison with the comparative methods.