Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming

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@Article{Lee201166,

• author = "Yi-Shian Lee and Lee-Ing Tong",
• title = "Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming",
• journal = "Knowledge-Based Systems",
• year = "2011",
• volume = "24",
• number = "1",
• pages = "66--72",
• month = feb,
• keywords = "genetic algorithms, genetic programming, ARIMA, Hybrid model, Forecasting, Artificial neural network",
• ISSN = "0950-7051",
• DOI = "doi:10.1016/j.knosys.2010.07.006",
• broken = "http://www.sciencedirect.com/science/article/B6V0P-50JHBSY-1/2/1501a7c1121cbfcf9683f1a0d781806b",
• abstract = "The autoregressive integrated moving average (ARIMA), which is a conventional statistical method, is employed in many fields to construct models for forecasting time series. Although ARIMA can be adopted to obtain a highly accurate linear forecasting model, it cannot accurately forecast nonlinear time series. Artificial neural network (ANN) can be used to construct more accurate forecasting model than ARIMA for nonlinear time series, but explaining the meaning of the hidden layers of ANN is difficult and, moreover, it does not yield a mathematical equation. This study proposes a hybrid forecasting model for nonlinear time series by combining ARIMA with genetic programming (GP) to improve upon both the ANN and the ARIMA forecasting models. Finally, some real data sets are adopted to demonstrate the effectiveness of the proposed forecasting model.",

}

Genetic Programming entries for Yi-Shian Lee Lee-Ing Tong

Citations