Robust Forecasts by Composite Model ANFIS/NGARCH Tuned by Adaptive Support Vector Regression

Volatility clustering suggests a time series where successive disturbances, even if uncorrelated, are yet serially dependent, and it causes a number of large residual errors in time-series forecasts. Thus, an adaptive neuro-fuzzy inference system (ANFIS) is combined with a nonlinear generalized autoregressive conditional heteroscedasticity (NGARCH) model that is tuned by adaptive support vector regression (ASVR) so as to tackle the problem of time-varying conditional variance in residual errors. The proposed method significantly reduces large residual errors in forecasts because volatility clustering effects are regulated to trivial levels. Two experiments (including a one-dimensional case and a two-dimensional case) using real world data series compare the proposed method and a number of well-known alternative methods. Results show that forecasting performance by the proposed method produces superior results, with good speed of computation. Goodness of fit of the proposed method is tested by Ljung-Box Q-test.