Transactions of Nanjing University of Aeronautics & Astronautics
Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems
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Abstract:
The prediction process often runs with small samples and under-sufficient information. To target this problem， we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis. Through a large number of prediction and optimization experiments， the accuracy and stability of the prediction method and the correction ability of the optimization method are studied. First， five traditional single-item prediction methods are used to process small samples with under-sufficient information， and the standard deviation method is used to assign weights on the five methods for combined forecasting. The accuracy of the prediction results is ranked. The mean and variance of the rankings reflect the accuracy and stability of the prediction method. Second， the error elimination prediction optimization method is proposed. To make， the prediction results are corrected by error elimination optimization method （EEOM）， Markov optimization and two-layer optimization separately to obtain more accurate prediction results. The degree improvement and decline are used to reflect the correction ability of the optimization method. The results show that the accuracy and stability of combined prediction are the best in the prediction methods， and the correction ability of error elimination optimization is the best in the optimization methods. The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information. Finally， the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.
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U8
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