Automatic Generation of a Type-2 Fuzzy System for Time Series Forecast based on Genetic Programming

  • Marco Antônio da Cunha Ferreira PUC-Rio
  • Ricardo Tanscheit PUC-Rio
  • Marley Vellasco PUC-Rio

Resumo


Este trabalho descreve um Sistema Fuzzy do tipo 2 desenvolvido automaticamente com o auxílio da Programação Genética para aplicação em previsão de séries temporais. O modelo resultante, denominado GPFIS-Forecast+, é baseado no GPFIS-Forecast desenvolvido anteriormente, que fez uso da Programação Genética Multigênica com bons resultados. Os resultados demonstram que, conforme o esperado, o sistema com conjuntos fuzzy do tipo 2 melhora o desempenho, principalmente na presença de dados ruidosos.

Referências


Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

Brown, R. G. & Meyer, R. F. (1961). The fundamental theorem of exponential smoothing. Operations Research, 9(5):673–685.

Calvo, T., Kolesárová, A., Komorníková, M., & Mesiar, R. (2002). Aggregation operators: properties, classes and construction methods. In: Calvo, T., Mayor, G., & Mesiar,

R., editors, Aggregation Operators, volume 97 of Studies in Fuzziness and Soft Computing, pages 3–104. Physica-Verlag HD.

Chai, C. T., Chuek, C. H., Mital, D., & Huat, T. T. (1997). Time series modelling and forecasting using genetic algorithms. In: First International Conference on Knowledge- Based Intelligent Electronic Systems., volume 1, pages 260–268.

da C. Ferreira, M. A., Koshiyama, A. S., Vellasco, M. M., & Tanscheit, R. (2015). Aprimoramentos de um sistema fuzzy-genético para análise de séries temporais. In: Bastos Filho, C. J. A., Pozo, A. R., & Lopes, H. S., editors, Anais do 12o Congresso Brasileiro de Inteligência Computacional, pages 1–6, Curitiba, PR.

da Cunha, M. A. (2015). GPFIS - Forecast: Um Sistema Fuzzy-Genético Genérico baseado em Programação Genética para Problemas de Previs˜ao de Séries Temporais. DEE PUC-Rio, 55p., Dissertação de Mestrado, Rio de Janeiro.

García, S., Fernández, A., Luengo, J., & Herrera, F. (2009). A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Computing, 13(10):959.

Hajek, P. (2018). Predicting corporate investment/non-investment grade by using intervalvalued fuzzy rule-based systems—a cross-region analysis. Applied Soft Computing, 62:73 – 85.

Herrera, F. & Magdalena, L. (1997). Genetic fuzzy systems: A tutorial. Tatra Mt. Math. Publ.(Slovakia), 13:93–121.

Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1):5–10.

Jang, J.-S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3):665–685.

Karaboga, D. & Kaya, E. (2018). Adaptive network based fuzzy inference system (anfis) training approaches: a comprehensive survey. Artificial Intelligence Review, pages 1–31.

Karnik, N. N., Mendel, J. M., & Liang, Q. (1999). Type-2 fuzzy logic systems. IEEE transactions on Fuzzy Systems, 7(6):643–658.

Khosravi, A., Nahavandi, S., Creighton, D., & Srinivasan, D. (2012). Interval type-2 fuzzy logic systems for load forecasting: A comparative study. IEEE Transactions on Power Systems, 27(3):1274–1282.

Koshiyama, A. S. (2014). GPFIS: Um Sistema Fuzzy-Genético Genérico baseado em Programação Genética. DEE PUC-Rio, 55p., Dissertação de Mestrado, Rio de Janeiro.

Koza, J. R. (1992). Genetic programming: on the programming of computers by means of natural selection. MIT press.

Mackey, M. C., Glass, L., et al. (1977). Oscillation and chaos in physiological control systems. Science, 197(4300):287–289.

Mahalakshmi, G., Sridevi, S., & Rajaram, S. (2016). A survey on forecasting of time series data. In: Computing Technologies and Intelligent Data Engineering (ICCTIDE), International Conference on, pages 1–8.

Mendel, J. M. & John, R. B. (2002). Type-2 fuzzy sets made simple. IEEE Transactions on fuzzy systems, 10(2):117–127.

Ritzman, L. & Krajewski, L. (2003). Administração da produção e operaç ˜oes. Pearson Prentice Hall.

Searson, D., Willis, M., & Montague, G. (2007). Co-evolution of non-linear pls model components. Journal of Chemometrics, 21(12):592–603.

Takagi, T. & Sugeno, M. (1993). Fuzzy identification of systems and its applications to modeling and control. In: Readings in Fuzzy Sets for Intelligent Systems, pages 387–403. Elsevier.

Unune, D. R., Barzani, M. M., Mohite, S. S., & Mali, H. S. (2018). Fuzzy logic-based model for predicting material removal rate and average surface roughness of machined nimonic 80a using abrasive-mixed electro-discharge diamond surface grinding. Neural Computing and Applications, 29(9):647–662.

Wang, L.-X. & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics, 22(6):1414–1427.

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. Management science, 6(3):324–342.

Zhang, G., Patuwo, B. E., & Hu, M. Y. (1998). Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting, 14(1):35–62.

Zor, K., Timur, O., & Teke, A. (2017). A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting. In: 6th International Youth Conference on Energy (IYCE), pages 1–7.

Publicado
22/10/2018
Como Citar

Selecione um Formato
FERREIRA, Marco Antônio da Cunha; TANSCHEIT, Ricardo; VELLASCO, Marley. Automatic Generation of a Type-2 Fuzzy System for Time Series Forecast based on Genetic Programming. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 15. , 2018, São Paulo. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 104-115. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2018.4408.