Feature engineering for improving financial derivatives-based rainfall prediction

2016 IEEE Congress on Evolutionary Computation, CEC 2016

Volumn , Issue , 2016, Pages 3483-3490

  Cramer, Sam ^a   Kampouridis, Michael ^a   Freitas, Alex A ^a  

^a UNIVERSITY OF KENT   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

FINANCIAL MARKETS; FORECASTING; GENETIC ALGORITHMS; GENETIC PROGRAMMING; MARKOV PROCESSES; WATER RESOURCES;

CHAIN-EXTENDED; DATA VARIABLES; FEATURE ENGINEERINGS; FINANCIAL DERIVATIVES; PROBLEM DOMAIN; RAINFALL PREDICTION; TIME-SERIES DATA; WATER RESOURCE PLANNING;

RAIN;

EID: 85008259832     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2016.7744231     Document Type: Conference Paper

References (16)

1. D. S. Wilks, "Multisite generalization of a daily stochastic precipitation generation model," Journal of Hydrology, vol. 210, pp. 178-191, Sep. 1998
2. I. Rodriguez-Iturbe, D. R. Cox, and V. Isham, "Some models for rainfall based on stochastic point processes," Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol. 410, no. 1839, pp. 269-288, 1987
3. R. W. Katz, "Precipitation as a chain-dependent process," Journal of Applied Meteorology and Climatology, vol. 16, no. 7, pp. 671-676, 1977
4. B. L. Cabrera, M. Odening, and M. Ritter, "Pricing Rainfall Derivatives at the CME," SFB 649 Discussion Papers, Jan. 2013
5. R. C. Noven, A. E. D. Veraart, and A. Gandy, "A lévy-driven rainfall model with applications to futures pricing," AStA Advances in Statistical Analysis, vol. 99, no. 4, pp. 403-432, 2015
6. S. Cramer, M. Kampouridis, A. A. Freitas, and A. Alexandridis, "Predicting rainfall in the context of rainfall derivatives using genetic programming," in Computational Intelligence for Financial Engineering and Economics, 2015 IEEE Symposium Series on, Dec 2015, pp. 711-718
7. N. Q. Hung, M. S. Babel, S. Weesakul, and N. K. Tripathi, "An artificial neural network model for rainfall forecasting in bangkok, thailand," Hydrology and Earth System Sciences, vol. 13, no. 8, pp. 1413-1425, 2009
8. J. Wu, J. Long, and M. Liu, "Evolving {RBF} neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm," Neurocomputing, vol. 148, pp. 136-142, 2015
9. Mislan, Haviluddin, S. Hardwinarto, Sumaryono, and M. Aipassa, "Rainfall monthly prediction based on artificial neural network: A case study in tenggarong station, east kalimantan-indonesia," Procedia Computer Science, vol. 59, pp. 142-151, 2015, international Conference on Computer Science and Computational Intelligence (ICCSCI 2015)
10. H. Weerasinghe, H. Premaratne, and D. Sonnadara, "Performance of neural networks in forecasting daily precipitation using multiple sources," Journal of the National Science Foundation of Sri Lanka, vol. 38, no. 3, 2010
11. O. Kisi and J. Shiri, "Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models," Water Resources Management, vol. 25, no. 13, pp. 3135-3152, 2011
12. A. Kattan, M. Kampouridis, Y.-S. Ong, and K. Mehamdi, "Transformation of input space using statistical moments: Ea-based approach," in Evolutionary Computation (CEC), 2014 IEEE Congress on, July 2014, pp. 2499-2506
13. D. J. Montana, "Strongly typed genetic programming," Evolutionary Computation, vol. 3, no. 2, pp. 199-230, 1995
14. M. A. Hall, "Correlation-based feature subset selection for machine learning," Ph.D. dissertation, University of Waikato, Hamilton, New Zealand, 1998
15. M. López-Ibáñez, J. Dubois-Lacoste, T. Stützle, and M. Birattari, "The Rpackageirace package, iterated race for automatic algorithm configuration," IRIDIA, Université Libre de Bruxelles, Belgium, Tech. Rep., 2011
16. J. Demsar, "Statistical comparisons of classifiers over multiple data sets," J. Mach. Learn. Res., vol. 7, pp. 1-30, Dec. 2006.