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Natural Computing
Erschienen in: Natural Computing 1/2017

06.01.2016

Time series forecasting with genetic programming

verfasst von: Mario Graff, Hugo Jair Escalante, Fernando Ornelas-Tellez, Eric S. Tellez

Erschienen in: Natural Computing | Ausgabe 1/2017

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Abstract

Genetic programming (GP) is an evolutionary algorithm that has received a lot of attention lately due to its success in solving hard world problems. There has been a lot of interest in using GP to tackle forecasting problems. Unfortunately, it is not clear whether GP can outperform traditional forecasting techniques such as auto-regressive models. In this contribution, we present a comparison between standard GP systems qand auto-regressive integrated moving average model and exponential smoothing. This comparison points out particular configurations of GP that are competitive against these forecasting techniques. In addition to this, we propose a novel technique to select a forecaster from a collection of predictions made by different GP systems. The result shows that this selection scheme is competitive with traditional forecasting techniques, and, in a number of cases it is statistically better.
Vorheriger Artikel Membrane automata for modeling biomolecular processes
Nächster Artikel On the overlap assembly of strings and languages

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Fußnoten
1
Given that the coefficients of the transformation are obtained from y then the transformed \(t_i\) might not be in the interval [0, 1].
 
Literatur
Agapitos A, ONeill M, Brabazon A (2012) Genetic programming for the induction of seasonal forecasts: a study on weather derivatives. In: Doumpos M, Zopounidis C, Pardalos PM (ed) Financial decision making using computational intelligence, vol 70 in springer optimization and its applications. Springer, USA, pp 159–188
Ali Ghorbani M, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput Geosci 36(5):620–627CrossRef
Barth A, Haris PI (2009) Biological and biomedical infrared spectroscopy, vol 2. IOS Press, Amsterdam
Bartoli A, Davanzo G, Lorenzo AD, Medvet E (2011) GP-based electricity price forecasting. In: Silva S, Foster JA, Nicolau M, Machado P, Giacobini M (ed) Genetic programming, vol 6621 in lecture notes in computer science. Springer, Berlin, pp 37–48
Chorng-Shyong O, Jih-Jeng H, Gwo-Hshiung T (2005) Model identification of ARIMA family using genetic algorithms. Appl Math Comput 164(3):885–912MathSciNetMATH
Ekrt A (2000) Shorter fitness preserving genetic programs. In: Fonlupt C, Hao J-K, Lutton E, Schoenauer M, Ronald E (ed) Artificial evolution, vol 1829 in lecture notes in computer science. Springer, Berlin, pp 73–83
Espinoza SDM (2014) Modelos de regresion para el pronostico de series temporales con estacionalidad creciente. Comput Sist 18(4):821–831
Falco I, Iazzetta A, Natale P, Tarantino E (1998) Evolutionary neural networks for nonlinear dynamics modeling. In: Eiben AE, Bck T, Schoenauer M, Schwefel H (eds) Parallel problem solving from nature PPSN V, vol 1498. Springer, Berlin, pp 593–602CrossRef
Ferreira C (2006) Gene expression programming. Springer, BerlinMATH
Flores JJ, Graff M, Cadena E (2005) Wind prediction using genetic programming and gene expression programming. In: International association for advanced of modelling and simulation techniques and methodologies for modelling and simulation of systems, Lyon, France–Mexico, (AMSE) ISBN: 970-703-323-1, pp 34–40
Flores JJ, Graff M, Rodriguez H (2012) Evolutive design of ARMA and ANN models for time series forecasting. Renew Energy 44:225–230CrossRef
Gaur S, Deo M (2008) Real-time wave forecasting using genetic programming. Ocean Eng 35(1112):1166–1172CrossRef
Graff M, Pena R, Medina A (2013) Wind speed forecasting using genetic programming. In: 2013 IEEE congress on evolutionary computation (CEC), pp 408–415
Graff M, Poli R (2010) Practical performance models of algorithms in evolutionary program induction and other domains. Artif Intell 174(15):1254–1276MathSciNetCrossRefMATH
Hooper DC, Flann NS (1996) Improving the accuracy and robustness of genetic programming through expression simplification. In: Proceedings of the first annual conference on genetic programming. MIT Press, Cambridge, p 428
Hyndman RJ, Khandakar Y (2008) Automatic time series forecasting: the forecast package for R. J Stat Softw 27(3):1–22CrossRef
Iba H, Nikolaev N (2000) Genetic programming polynomial models of financial data series. In: Proceedings of the 2000 congress on evolutionary computation, vol 2, pp 1459–1466
Iba H, Sasaki T (1999) Using genetic programming to predict financial data. In: Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99, vol 1, p 251
Igel C, Hsken M (2003) Empirical evaluation of the improved RPROP learning algorithms. Neurocomputing 50:105–123CrossRefMATH
Kaboudan MA (2000) Genetic programming prediction of stock prices. Comput Econ 16(3):207–236CrossRefMATH
Kashid SS (2012) Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming. J Hydrol 454–455:26–41CrossRef
Koza JR (1992) Genetic programming: on the programming of computers by natural selection. MIT Press, CambridgeMATH
Lam HK, Ling SH, Leung FH, Tam PK (2001) Tuning of the structure and parameters of neural network using an improved genetic algorithm. In: The 27th annual conference of the IEEE industrial electronics society, 2001. IECON ’01, vol 1, pp 25–30. IEEE
Lee Y-S, Tong L-I (2011) Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers Manag 52(1):147–152CrossRef
Lee DG, Lee BW, Chang SH (1997) Genetic programming model for long-term forecasting of electric power demand. Electr Power Syst Res 40(1):17–22CrossRef
Makridakis S, Hibon M (2000) The M3-competition: results, conclusions and implications. Int J Forecast 16(4):451–476CrossRef
Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M, Lewandowski R, Newton J, Parzen E, Winkler R (1982) The accuracy of extrapolation (time series) methods: results of a forecasting competition. J Forecast 1(2):111153CrossRef
Mayer HA, Schwaiger R (1999) Evolutionary and coevolutionary approaches to time series prediction using generalized multi-layer perceptrons. In: Proceedings of the 1999 congress on evolutionary computation, 1999. CEC 99, vol 1, p 280. IEEE
Minerva T, Poli I (2001) Building ARMA models with genetic algorithms. In: Boers EJW, Gottlieb J, Lanzi PL, Smith RE, Cagnoni S, Hart E, Raidl GR, Tijink H (ed) EvoWorkshops, vol 2037 of lecture notes in computer science, pp 335–343. Springer
Nasseri M, Moeini A, Tabesh M (2011) Forecasting monthly urban water demand using extended Kalman filter and genetic programming. Expert Syst Appl 38(6):7387–7395CrossRef
Nissen S (2003) Implementation of a fast artificial neural network library (FANN). Technical report, Department of Computer Science University of Copenhagen (DIKU). http://​fann.​sf.​net
Nitsure SP (2012) Wave forecasts using wind information and genetic programming. Ocean Eng 54:61–69CrossRef
Peralta J, Li X, Gutierrez G, Sanchis, A (2010) Time series forecasting by evolving artificial neural networks using genetic algorithms and differential evolution. In: The 2010 international joint conference on neural networks (IJCNN), pp 1–8. IEEE
Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE international conference on neural networks, pp 586–591
Shiri J, Kisi O (2011) Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Comput Geosci 37(10):1692–1701CrossRef
Smart W, Zhang M (2004) Continuously evolving programs in genetic programming using gradient descent. In: Proceedings of 2004 Asia-Pacific workshop on genetic programming
Topchy A, Punch WF (2001) Faster genetic programming based on local gradient search of numeric leaf values. In: Proceedings of the genetic and evolutionary computation conference (GECCO-2001), pp 155–162
Wagner N, Khouja M, Michalewicz Z, McGregor RR (2008) Forecasting economic time series with the DyFor genetic program model. Appl Financ Econ 18(5):357–378CrossRef
Wang H, Zhao W (2009) ARIMA model estimated by particle swarm optimization algorithm for consumer price index forecasting. In: Deng H, Wang L, Wang FL, Lei J (ed) AICI, vol 5855 of lecture notes in computer science, pp 48–58. Springer
Wang X, Wang H, Dai G, Tang Z (2006) A reliable resilient backpropagation method with gradient ascent. In: Proceedings of the 2006 International conference on intelligent computing: part II. Springer, Berlin, pp 236–244
Weigend AS, Gershenfeld NA (1994) Time series prediction: forecasting the future and understanding the past: proceedings of the NATO advanced research workshop on comparative time series analysis, held in Santa Fe, New Mexico, May 14–17, 1992. Addison-Wesley
Wilcoxon F (1945) Individual comparisons by ranking methods. Biom Bull 1(6):80CrossRef
Zhang M, Smart W (2004) Genetic programming with gradient descent search for multiclass object classification. In: Keijzer M, OReilly U-M, Lucas S, Costa E, Soule T (ed) Genetic programming, vol 3003 in lecture notes in computer science. Springer, Berlin, pp 399–408
Metadaten
Titel
Time series forecasting with genetic programming
verfasst von
Mario Graff
Hugo Jair Escalante
Fernando Ornelas-Tellez
Eric S. Tellez
Publikationsdatum
06.01.2016
Verlag
Springer Netherlands
Erschienen in
Natural Computing / Ausgabe 1/2017
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-015-9536-z

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