@inproceedings{a8aee9dccb5548c192be3cde05851fd7,
title = "A new genetic fuzzy system approach for parameter estimation of ARIMA model",
abstract = "The Autoregressive Integrated moving Average model is the most powerful and practical time series model for forecasting. Parameter estimation is the most crucial part in ARIMA modeling. Inaccurate and wrong estimated parameters lead to bias and unacceptable forecasting results. Parameter optimization can be adopted in order to increase the demand forecasting accuracy. A paradigm of the fuzzy system and a genetic algorithm is proposed in this paper as a parameter estimation approach for ARIMA. The new approach will optimize the parameters by tuning the fuzzy membership functions with a genetic algorithm. The proposed Hybrid model of ARIMA and the genetic fuzzy system will yield acceptable forecasting results.",
keywords = "ARIMA, Genetic fuzzy system, forecasting, hybrid model, parameter estimation",
author = "Saima Hassan and Jafreezal Jaafar and Belhaouari, {Brahim S.} and Abbas Khosravi",
year = "2012",
doi = "10.1063/1.4757513",
language = "English",
isbn = "9780735410947",
series = "AIP Conference Proceedings",
pages = "455--459",
booktitle = "International Conference on Fundamental and Applied Sciences 2012, ICFAS 2012",
note = "2nd International Conference on Fundamental and Applied Sciences 2012, ICFAS 2012 ; Conference date: 12-06-2012 Through 14-06-2012",
}