DNA optimization threshold autoregressive prediction model and its application in ice condition time series
Summary: There are many parameters which are very difficult to calibrate in threshold autoregressive prediction models for nonlinear time series. The threshold values, autoregressive coefficients, and the delay time are key parameters in the threshold autoregressive prediction models. To improve prediction precision and reduce the uncertainties in the determination of the above parameters, a new DNA (deoxyribonucleic acid) optimization threshold autoregressive prediction model (DNAOTARPM) is proposed by combining the threshold autoregressive method and DNA optimization method. The above optimal parameters are selected by minimizing the objective function. Real Ice condition time series at Bohai are taken to validate the new method. The prediction results indicate that the new method can choose the above optimal parameters in prediction processes. Compared with an improved genetic algorithm threshold autoregressive prediction model (IGATARPM) and a standard genetic algorithm threshold autoregressive prediction model (SGATARPM), DNAOTARPM has higher precision and faster convergence speed for predicting nonlinear ice condition time series.
scientific article; zbMATH DE number 6173086