Forecasting systems reliability based on support vector regression with genetic algorithms

Citations

Cited by:

1. Li, Cheng & Zheng, Weimin & Ge, Peng, 2022. "Tourism demand forecasting with spatiotemporal features," Annals of Tourism Research, Elsevier, vol. 94(C).
2. Rong, Jia & Vu, Huy Quan & Law, Rob & Li, Gang, 2012. "A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining," Tourism Management, Elsevier, vol. 33(4), pages 731-740.
3. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," IREA Working Papers 201502, University of Barcelona, Research Institute of Applied Economics, revised Jan 2015.
   • Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Multiple-input multiple-output vs. single-input single-output neural network forecasting”," AQR Working Papers 201502, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2015.
4. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
5. Wu, Xuedong & Chang, Yanchao & Mao, Jianxu & Du, Zhaoping, 2013. "Predicting reliability and failures of engine systems by single multiplicative neuron model with iterated nonlinear filters," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 244-250.
6. Li, Cheng & Ge, Peng & Liu, Zhusheng & Zheng, Weimin, 2020. "Forecasting tourist arrivals using denoising and potential factors," Annals of Tourism Research, Elsevier, vol. 83(C).
7. Bourinet, J.-M., 2016. "Rare-event probability estimation with adaptive support vector regression surrogates," Reliability Engineering and System Safety, Elsevier, vol. 150(C), pages 210-221.
8. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
9. Wei, Zhao & Tao, Tao & ZhuoShu, Ding & Zio, Enrico, 2013. "A dynamic particle filter-support vector regression method for reliability prediction," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 109-116.
10. Liu, Yuan & Wang, RuiXue, 2016. "Study on network traffic forecast model of SVR optimized by GAFSA," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 153-159.
11. Joonhyuck Lee & Dongsik Jang & Sangsung Park, 2017. "Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability," Sustainability, MDPI, vol. 9(6), pages 1-12, May.
12. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
13. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
14. Rocco S, Claudio M., 2013. "Singular spectrum analysis and forecasting of failure time series," Reliability Engineering and System Safety, Elsevier, vol. 114(C), pages 126-136.
15. Moura, Márcio das Chagas & Zio, Enrico & Lins, Isis Didier & Droguett, Enrique, 2011. "Failure and reliability prediction by support vector machines regression of time series data," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1527-1534.
16. Akın, Melda, 2015. "A novel approach to model selection in tourism demand modeling," Tourism Management, Elsevier, vol. 48(C), pages 64-72.
17. Yang, Bo & Li, Xiang & Xie, Min & Tan, Feng, 2010. "A generic data-driven software reliability model with model mining technique," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 671-678.
18. Joonhyuck Lee & Gabjo Kim & Sangsung Park & Dongsik Jang, 2016. "Hybrid Corporate Performance Prediction Model Considering Technical Capability," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
19. Hazar ALTINBAŞ, 2020. "Modern Kredi Sınıflandırma Çalışmaları ve Metasezgisel Algoritma Uygulamaları: Sistematik Bir Derleme," Istanbul Business Research, Istanbul University Business School, vol. 49(1), pages 146-175, May.
20. Cheng, Anyu & Jiang, Xiao & Li, Yongfu & Zhang, Chao & Zhu, Hao, 2017. "Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 422-434.
21. Fink, Olga & Zio, Enrico & Weidmann, Ulrich, 2014. "Predicting component reliability and level of degradation with complex-valued neural networks," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 198-206.
22. Enrique L Droguett & Isis D Lins & Márcio C Moura & Enrico Zio & Carlos M Jacinto, 2015. "Variable selection and uncertainty analysis of scale growth rate under pre-salt oil wells conditions using support vector regression," Journal of Risk and Reliability, , vol. 229(4), pages 319-326, August.
23. Dai, Hongzhe & Zhang, Hao & Wang, Wei, 2012. "A support vector density-based importance sampling for reliability assessment," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 86-93.
24. Wan, Shui Ki & Song, Haiyan, 2018. "Forecasting turning points in tourism growth," Annals of Tourism Research, Elsevier, vol. 72(C), pages 156-167.
25. Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.
26. Khatibinia, Mohsen & Javad Fadaee, Mohammad & Salajegheh, Javad & Salajegheh, Eysa, 2013. "Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 22-33.