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Constructing Optimal Prediction Intervals by Using Neural Networks and Bootstrap Method

2014; Institute of Electrical and Electronics Engineers; Volume: 26; Issue: 8 Linguagem: Inglês

10.1109/tnnls.2014.2354418

2162-2388

Abbas Khosravi, Saeid Nahavandi, Dipti Srinivasan, Rihanna Khosravi,

Hydrological Forecasting Using AI

This brief proposes an efficient technique for the construction of optimized prediction intervals (PIs) by using the bootstrap technique. The method employs an innovative PI-based cost function in the training of neural networks (NNs) used for estimation of the target variance in the bootstrap method. An optimization algorithm is developed for minimization of the cost function and adjustment of NN parameters. The performance of the optimized bootstrap method is examined for seven synthetic and real-world case studies. It is shown that application of the proposed method improves the quality of constructed PIs by more than 28% over the existing technique, leading to narrower PIs with a coverage probability greater than the nominal confidence level.