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A novel forecasting approach is composed of BWGC and NGARCH tuned by quantum-neuro-based adaptive support vector regression with nested local adiabatic evolution. Instead of traditionally (globally) adiabatic evolution algorithm for unstructured search, we focus on the structured adiabatic quantum search by nesting a partial search over a reduced set of variables into a global search for solving an optimization problem on adaptive support vector regression (ASVR) yielding an average complexity of order radicNa, with a < 1. The proposed method is experimentally applied to predicting typhoon moving path even though fewer sampled data are available to work out possibly real-time look-ahead typhoon moving position. Consequently, it yields satisfactory results of forecasting typhoon moving path when comparing with several alternative methods.