DocumentCode :
1955472
Title :
Nonlinear combination of forecasts using artificial neural network, fuzzy logic and neuro-fuzzy approaches
Author :
Palit, Ajoy Kumar ; Popovic, D.
Author_Institution :
Bremen Univ., Germany
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
566
Abstract :
In the actual practice, it becomes interesting from the efficiency point of view to combine various forecasts of a specific time series into a single forecast and to interrogate the resulting forecasting accuracy. The combination is usually nonlinear. Various intelligent combination techniques have been suggested for this purpose, based on different neural network architectures, including the feedforward neural network and evolutionary neural network. In this paper, the nonlinear combination of time series forecasts is proposed, based on isolated use of neural networks, fuzzy logic and neuro-fuzzy systems. On some practical examples it is demonstrated that the nonlinear combination of a group of forecasts based on intelligent approach is capable of producing a single better forecast than any individual forecasts involved in the combination
Keywords :
forecasting theory; fuzzy logic; fuzzy neural nets; optimisation; time series; forecasting theory; fuzzy logic; fuzzy neural network; optimisation; time series; Artificial intelligence; Artificial neural networks; Fellows; Fuzzy logic; Fuzzy neural networks; Intelligent networks; Neural networks; Predictive models; Time series analysis; US Department of Energy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1098-7584
Print_ISBN :
0-7803-5877-5
Type :
conf
DOI :
10.1109/FUZZY.2000.839055
Filename :
839055
Link To Document :
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