Abstract
Evolutionary design of time series predictors is a field that has been explored for several years now. The levels of design vary in the many works reported in the field. We decided to perform a complete design and training of ARIMA models using Evolutionary Computation. This decision leads to high dimensional search spaces, whose size increases exponentially with dimensionality. In order to reduce the size of those search spaces we propose a method that performs a preliminary statistical analysis of the inputs involved in the model design and their impact on quality of results; as a result of the statistical analysis, we eliminate inputs that are irrelevant for the prediction task. The proposed methodology proves to be effective and efficient, given that the results increase in accuracy and the computing time required to produce the predictors decreases.
Keywords
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References
Cadenas, E., Rivera, W.: Wind speed forecasting in the south coast of Oaxaca, Mexico. Renewable Energy 32(12), 2116–2128 (2007)
Cadenas, E., Rivera, W.: Short term wind speed forecasting in La Venta, Oaxaca, using artificial neural networks. Renewable Energy 34, 274–278 (2009)
Chen, Y., Yang, B., Dong, J., Abraham, A.: Time-series forecasting using flexible neural tree model. Information Sciences 174, 219–2352 (2005)
Chorng-Shyong, O., Jih-Jeng, H., Gwo-Hshiung, T.: Model identification of ARIMA family using genetic algorithms. Applied Mathematics and Computation 164(3), 885–912 (2005)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics 32(2), 407–499 (2004)
Elliot, D., Schwartz, M., Scott, G., Haymes, S., George, R.: Wind energy resource atlas of Oaxaca. NREL/TP, 500-34519 (2003)
Flores, J.J., Graff, M., Cadena, E.: Wind prediction using genetic programming and gene expression programming. In: Techniques and Methodologies for Modelling and Simulation of Systems, Lyon France - Mexico, AMSE, International Association for Advanced of Modelling and Simulation, pp. 34–40 (2005) ISBN:970-703-323-1
Flores, J.J., Loaeza, R., Rodriguez, H., Cadenas, E.: Wind speed forecasting using a hybrid neural-evolutive approach. In: Aguirre, A.H., Borja, R.M., García, C.A.R. (eds.) MICAI 2009. LNCS, vol. 5845, pp. 600–609. Springer, Heidelberg (2009)
Gelper, S., Croux, C.: Least angle regression for time series forecasting with many predictors. Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/164224, Katholieke Universiteit Leuven (2008)
Ghiassi, H., Saidane, M., Zimbra, D.K.: A dynamic artificial neural network model for forecasting time series events. International Jounal of Forecasting 21, 341–362 (2005)
Haykin, S.: Neural Networks a comprehensive foundation. Prentice-Hall, Englewood Cliffs (1999)
Jaramillo, O.A., Borja, M.A.: Wind speed analysis in La Ventosa Mexico: a bimodal probability distribution case. Renewable Energy 29, 1613–1630 (2004)
Langdon, W.B., Buxton, B.F.: Genetic programming for mining DNA chip data from cancer patients. Genetic Programming and Evolvable Machines 5(3), 251–257 (2004)
Langdon, W.B., Harrison, A.P.: GP on SPMD parallel graphics hardware for mega bioinformatics data mining. Soft Comput. 12(12), 1169–1183 (2008)
Makridakis, S., Whellwright, S.C., Hyndamn, R.J.: Forecasting Methods and Applications, 3rd edn. John Wiley & Sons, Inc., Chichester (1992)
Minerva, T., Poli, I.: Building ARMA models with genetic algorithms. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 335–343. Springer, Heidelberg (2001)
Price, G.R.: Selection and covariance. Nature 227, 520–521 (1970)
Riahy, G.H., Abedi, M.: Short term wind speed forcasting for wind turbine applications using linear prediction method. Renewable Energy 33, 35–41 (2008)
Steenburgh, W., Schultz, D., Colle, B.: The structure and evolution of gap outflow over the Gulf of Tehuantepec. Monthly Weather review 91 (1998)
Verleysen, M., François, D.: The curse of dimensionality in data mining and time series prediction. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 758–770. Springer, Heidelberg (2005)
Wang, H., Zhao, W.: Arima model estimated by particle swarm optimization algorithm for consumer price index forecasting. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds.) AICI 2009. LNCS, vol. 5855, pp. 48–58. Springer, Heidelberg (2009)
Zhang, G.P.: Time series forecasting using a hybrid arima and neural network model. Neurocomputing 50, 159–175 (2003)
Zhang, G.P.: A neural network ensemble method with jittered training data for time series forecasting. Information Sciences 177, 5329–5346 (2007)
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Flores, J.J., Rodriguez, H., Graff, M. (2010). Reducing the Search Space in Evolutive Design of ARIMA and ANN Models for Time Series Prediction. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_28
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DOI: https://doi.org/10.1007/978-3-642-16773-7_28
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