Abstract
Prediction Intervals (PIs) can specify the level of uncertainty related to point-based prediction. Most Neural Network (NN)-based approaches for constructing PIs suffer from computational expense and some restrictive assumptions on data distribution. This paper develops a randomized algorithm for PIs building with good performance in terms of both effectiveness and efficiency. To achieve this goal, a neural network ensemble with random weights is employed as a learner model, and a novel algorithm for generating teacher signals is proposed. Our proposed Randomized Algorithm for Prediction Intervals (RAPI) constructs an NN ensemble with two outputs, representing the lower and upper bounds of PIs, respectively. Experimental results with comparisons over nine benchmark datasets indicate that RAPI performs favourably in terms of coverage rate, specificity and efficiency.
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References
Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.F.: Comprehensive review of neural network-based prediction intervals and new advances. IEEE Trans. Neural Netw. 22(9), 1341–1356 (2011)
Zhao, J.H., Dong, Z.Y., Xu, Z., Wong, K.P.: A statistical approach for interval forecasting of the electricity price. IEEE Trans. Power Syst. 23(2), 267–276 (2008)
Khosravi, A., Nahavandi, S., Creighton, D.: Construction of optimal prediction intervals for load forecasting problems. IEEE Trans. Power Syst. 25(3), 1496–1503 (2010)
Khosravi, A., Nahavandi, S., Creighton, D.: A prediction interval-based approach to determine optimal structures of neural network metamodels. Expert Syst. Appl. 37(3), 2377–2387 (2010)
Van Hinsbergen, C.I., Van Lint, J., Van Zuylen, H.: Bayesian committee of neural networks to predict travel times with confidence intervals. Transp. Res. Part C: Emerg. Technol. 17(5), 498–509 (2009)
Benoit, D.F., Van den Poel, D.: Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: an application in financial services. Expert Syst. Appl. 36(7), 10475–10484 (2009)
Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.F.: Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans. Neural Netw. 22(3), 337–346 (2011)
Igelnik, B., Pao, Y.H.: Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans. Neural Netw. 6(6), 1320–1329 (1995)
Alhamdoosh, M., Wang, D.: Fast decorrelated neural network ensembles with random weights. Inf. Sci. 264, 104–117 (2014)
Ming, L., Wang, D.: Insights into randomized algorithms for neural networks: practical issues and common pitfalls. Inf. Sci. 382, 170–178 (2017)
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Miskony, B., Wang, D. (2017). A Randomized Algorithm for Prediction Interval Using RVFL Networks Ensemble. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_6
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DOI: https://doi.org/10.1007/978-3-319-70093-9_6
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