کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
405322 677530 2011 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming
چکیده انگلیسی

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 utilized 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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 24, Issue 1, February 2011, Pages 66–72
نویسندگان
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