TY - GEN
T1 - A hybrid prediction method combining RBF neural network and FAR model
AU - Lu, Yongle
AU - Lang, Rongling
PY - 2007
Y1 - 2007
N2 - The classical autoregressive moving average model (ARMA) fails to satisfy the high request for precision in predicting nonlinear and nonstationary systems. Overcoming the difficulty, a hybrid prediction method is proposed in this paper, which organically couples the radial basis function prediction neural network (RBFPNN) and the functional-coefficient autoregressive prediction model (FARPM). An observation time series characterized by nonlinearity and nonstationarity can be technically decomposed with the wavelet analysis tool into two clusters of sequences, i.e. the smooth sequences and the stationary sequences, which can be effectively predicted with RBFPNN and FARPM respectively. Then, the integrated prediction is obtained by merging the results of RBFPNN and FARPM. It's indicated by the simulation that the prediction precision for one step, 4 steps and 12 steps can be improved at least by 41%, 60% and 60% respectively, compared to the prediction with ARMA, RBFPNN and FARPM separately.
AB - The classical autoregressive moving average model (ARMA) fails to satisfy the high request for precision in predicting nonlinear and nonstationary systems. Overcoming the difficulty, a hybrid prediction method is proposed in this paper, which organically couples the radial basis function prediction neural network (RBFPNN) and the functional-coefficient autoregressive prediction model (FARPM). An observation time series characterized by nonlinearity and nonstationarity can be technically decomposed with the wavelet analysis tool into two clusters of sequences, i.e. the smooth sequences and the stationary sequences, which can be effectively predicted with RBFPNN and FARPM respectively. Then, the integrated prediction is obtained by merging the results of RBFPNN and FARPM. It's indicated by the simulation that the prediction precision for one step, 4 steps and 12 steps can be improved at least by 41%, 60% and 60% respectively, compared to the prediction with ARMA, RBFPNN and FARPM separately.
KW - Functional-coefficient autoregressive model
KW - Nonlinear and nonstationary system
KW - Prediction
KW - Radial basis function neural network
KW - Time series
UR - https://www.scopus.com/pages/publications/38049181123
U2 - 10.1007/978-3-540-71701-0_63
DO - 10.1007/978-3-540-71701-0_63
M3 - 会议稿件
AN - SCOPUS:38049181123
SN - 9783540717003
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 598
EP - 605
BT - Advances in Knowledge Discovery and Data Mining - 11th Pacific-Asia Conference, PAKDD 2007, Proceedings
PB - Springer Verlag
T2 - 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2007
Y2 - 22 May 2007 through 25 May 2007
ER -