Neural Information Processing

Volume 5863 of the series Lecture Notes in Computer Science pp 285-292

Integrating Simulated Annealing and Delta Technique for Constructing Optimal Prediction Intervals

  • Abbas KhosraviAffiliated withCentre for Intelligent Systems Research (CISR), Deakin University
  • , Saeid NahavandiAffiliated withCentre for Intelligent Systems Research (CISR), Deakin University
  • , Doug CreightonAffiliated withCentre for Intelligent Systems Research (CISR), Deakin University

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This paper aims at developing a new criterion for quantitative assessment of prediction intervals. The proposed criterion is developed based on both key measures related to quality of prediction intervals: length and coverage probability. This criterion is applied as a cost function for optimizing prediction intervals constructed using delta technique for neural network model. Optimization seeks out to minimize length of prediction intervals without compromising their coverage probability. Simulated Annealing method is employed for readjusting neural network parameters for minimization of the new cost function. To further ameliorate search efficiency of the optimization method, parameters of the network trained using weight decay method are considered as the initial set in Simulated Annealing algorithm. Implementation of the proposed method for a real world case study shows length and coverage probability of constructed prediction intervals are better than those constructed using traditional techniques.


prediction interval neural network simulated annealing delta technique