An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

Expert Systems with Applications

Volumn 85, Issue , 2017, Pages 169-181

  Cramer, Sam ^a   Kampouridis, Michael ^a   Freitas, Alex A ^a   Alexandridis, Antonis K ^b  

^a UNIVERSITY OF KENT   (United Kingdom)

^b UNIVERSITY OF KENT   (United Kingdom)

Author keywords

Machine learning; Rainfall; Weather derivatives

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; GENETIC ALGORITHMS; GENETIC PROGRAMMING; INTELLIGENT SYSTEMS; LEARNING SYSTEMS; MARKOV PROCESSES; NEAREST NEIGHBOR SEARCH; RAIN; TIME SERIES;

K-NEAREST NEIGHBOURS; MACHINE LEARNING METHODS; PREDICTIVE PERFORMANCE; RADIAL BASIS NEURAL NETWORKS; RESEARCH OPPORTUNITIES; STATE-OF-THE-ART TECHNIQUES; SUPPORT VECTOR REGRESSION (SVR); WEATHER DERIVATIVES;

LEARNING ALGORITHMS;

EID: 85019384458     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2017.05.029     Document Type: Article

References (28)

1. Akaike, H., A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:6 (1974), 716–723.
2. Broomhead, D.S., Lowe, D., Multivariable functional interpolation and adaptive networks. Complex Systems 2, 1988, 321–355.
3. Buishand, T.A., Some remarks on the use of daily rainfall models. Journal of Hydrology 36 (1978), 295–308.
4. Cabrera, B.L., Odening, M., Ritter, M., Pricing rainfall futures at the CME. Journal of Banking & Finance 37:11 (2013), 4286–4298.
5. Cao, M., Li, A., Wei, J.Z., Precipitation modeling and contract valuation. The Journal of Alternative Investments 7:2 (2004), 93–99.
6. Chang, C.-C., Lin, C.-J., LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2 (2011), 27:1–27:27.
7. Cosma, G., Brown, D., Archer, M., Khan, M., Pockley, A.G., A survey on computational intelligence approaches for predictive modeling in prostate cancer. Expert Systems with Applications 70 (2017), 1–19.
8. Cramer, S., Kampouridis, M., Freitas, A., A genetic decomposition algorithm for predicting rainfall within financial weather derivatives. Gecco ‘16: Proceedings of the 2016 genetic and evolutionary computation conference, 2016, ACM, Denver, USA, 885–892.
9. Cramer, S., Kampouridis, M., Freitas, A.A., Feature engineering for improving financial derivatives-based rainfall prediction. Ong, Y.-S., (eds.) Proceedings of 2016 ieee congress on evolutionary computation (cec 2016), 2016, IEEE Press, Vancouver, 3483–3490.
10. Cramer, S., Kampouridis, M., Freitas, A.A., Alexandridis, A., Predicting rainfall in the context of rainfall derivatives using genetic programming. 2015 ieee symposium series on computational intelligence, 2015, 711–718.
11. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H., The WEKA data mining software: An update. SIGKDD Explorations Newsletter 11:1 (2009), 10–18.
12. Holmes, G., Hall, M., Frank, E., Generating rule sets from model trees. Proceedings of the 12th australian joint conference on artificial intelligence: Advanced topics in artificial intelligence, 1999, Springer-Verlag, 1–12.
13. Hung, N.Q., Babel, M.S., Weesakul, S., Tripathi, N.K., An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences 13:8 (2009), 1413–1425.
14. Kisi, O., Shiri, J., Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resources Management 25:13 (2011), 3135–3152.
15. Liao, S.-H., Chu, P.-H., Hsiao, P.-Y., Data mining techniques and applications a decade review from 2000 to 2011. Expert Systems with Applications 39:12 (2012), 11303–11311.
16. López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M., The rpackageirace package, iterated race for automatic algorithm configuration. Technical Report, 2011, IRIDIA, Université Libre de Bruxelles, Belgium.
17. Mislan, Haviluddin, Hardwinarto, S., Sumaryono, Aipassa, M., Rainfall monthly prediction based on artificial neural network: A case study in tenggarong station, east kalimantan - indonesia. Procedia Computer Science 59 (2015), 142–151 International Conference on Computer Science and Computational Intelligence (ICCSCI 2015).
18. Odening, M., Musshoff, O., Xu, W., Analysis of rainfall derivatives using daily precipitation models: Opportunities and pitfalls. Agricultural Finance Review 67:1 (2007), 135–156.
19. Patel, J., Shah, S., Thakkar, P., Kotecha, K., Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications 42:1 (2015), 259–268.
20. Quinlan, J.R., Learning with continuous classes. In proceedings of the australian joint conference on artificial intelligence, 1992, World Scientific, 343–348.
21. Ritter, M., Muhoff, O., Odening, M., Minimizing geographical basis risk of weather derivatives using a multi-site rainfall model. Computational Economics 44:1 (2014), 67–86.
22. Roldan, J., Woolhiser, D.A., Stochastic daily precipitation models: 1. Acomparison of occurrence processes. Water Resources Research 18:5 (1982), 1451–1459.
23. Said, S.E., Dickey, D.A., Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 71:3 (1984), 599–607.
24. Vapnik, V., Golowich, S.E., Smola, A., Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems 9 (1996), 281–287.
25. Weerasinghe, H., Premaratne, H., Sonnadara, D., Performance of neural networks in forecasting daily precipitation using multiple sources. Journal of the National Science Foundation of Sri Lanka 38:3 (2010), 163–170.
26. Wilks, D., Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. Agricultural and Forest Meteorology 93:3 (1999), 153–169.
27. Wilks, D.S., Multisite generalization of a daily stochastic precipitation generation model. Journal of Hydrology 210 (1998), 178–191.
28. Wu, J., Long, J., Liu, M., Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148 (2015), 136–142.