Abstract
There are many parameters which are very difficult to calibrate in the threshold autoregressive prediction model for nonlinear time series. The threshold value, autoregressive coefficients, and the delay time are key parameters in the threshold autoregressive prediction model. 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 threshold autoregressive method and DNA optimization method. The above optimal parameters are selected by minimizing 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 process. Compared with improved genetic algorithm threshold autoregressive prediction model (IGATARPM) and standard genetic algorithm threshold autoregressive prediction model (SGATARPM), DNAOTARPM has higher precision and faster convergence speed for predicting nonlinear ice condition time series.
Highlights
Many natural phenomena, such as ice condition, runoff, are usually nonlinear, complex, and dynamic processes
The simulation of the nonlinear time series was very difficult with the traditional deterministic mathematic models, which cause new challenges to calibrate the parameters 1, 2
The uncertainties in determining the parameters of the threshold variables, autoregressive coefficients, and the delay time exist in the developed threshold autoregressive model
Summary
Many natural phenomena, such as ice condition, runoff, are usually nonlinear, complex, and dynamic processes. DNA optimization threshold autoregressive prediction model DNAOTARPM is presented to determine the parameters and to improve the calculation precision for predicting ice condition time series. The new model, DNA optimization threshold autoregressive prediction method DNAOTARPM , is described as follows. Step 4 Solve objective function by DNA optimization method. The DNAOM computation is over until the algorithm running times reaches the designed T times or there exists an optimal chromosome Cfit whose fitness satisfies a given criterion. In the former case the Cfit is the fittest chromosome or the most excellent chromosome in the population. The length m 10, population size N 100, the number of excellent individuals ne 10, the times of evolution alternating Q 3, the crossover probability pc 1.0, and the mutation probability pm 0.5
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