Title :
Time Series Forecasting Model with Error Correction by Structure Adaptive RBF Neural Network
Author :
Qu, Lili ; Chen, Yan ; Liu, Zhenfeng
Author_Institution :
Sch. of Econ. & Manage., Dalian Maritime Univ., Liaoning
Abstract :
A hybrid methodology is proposed to take advantage of the unique strength of autoregressive integrated moving average (ARIMA) and RBF (radial basis function) neural networks in linear and nonlinear modeling, which is an error correction method to create synergies in the overall forecasting process. ARIMA model is used to generate a linear forecast in the first stage, and then RBFN is developed as the nonlinear pattern recognition to correct the estimation error in ARIMA forecast. A dynamic clustering algorithm is developed to optimize the network structure, which makes the RBFN adapt to the specified training set, reduces computation complexity and avoids overfitting. With two real datasets, in terms of forecasting accuracy, empirical results evidently show that the hybrid model outperforms noticeably ARIMA and RBFN model used in isolation
Keywords :
adaptive systems; autoregressive moving average processes; error correction; forecasting theory; pattern clustering; radial basis function networks; time series; ARIMA; autoregressive integrated moving average; dynamic clustering; error correction; linear forecasting; nonlinear pattern recognition; radial basis function neural networks; structure adaptive RBF neural network; time series forecasting model; Adaptive systems; Clustering algorithms; Economic forecasting; Error correction; Heuristic algorithms; Neural networks; Pattern recognition; Predictive models; Radial basis function networks; Time series analysis; ARIMA; Dynamic clustering algorithm; Error correction; Radial Basis Function Neural Network; Time series analysis;
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
DOI :
10.1109/WCICA.2006.1714408
