Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Symbolic and numerical regression: experiments and applications
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
Building credit scoring models using genetic programming
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Piecewise cloud approximation for time series mining
Knowledge-Based Systems
Shape-based template matching for time series data
Knowledge-Based Systems
Forecasting tourism demand based on empirical mode decomposition and neural network
Knowledge-Based Systems
Multi-stage genetic programming: A new strategy to nonlinear system modeling
Information Sciences: an International Journal
Hybrid method for the analysis of time series gene expression data
Knowledge-Based Systems
Short communication: Selective Subsequence Time Series clustering
Knowledge-Based Systems
A new linear & nonlinear artificial neural network model for time series forecasting
Decision Support Systems
Nonlinear speech coding model based on genetic programming
Applied Soft Computing
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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.