Abstract
In this paper, a hierarchical neural network architecture for forecasting time series is presented. The architecture is composed of two hierarchical levels using a maximum likelihood competitive learning algorithm. The first level of the system has three experts each using backpropagation and a gating network to partition the input space in order to map the input vectors to the output vectors. The second level of the hierarchical network has an expert using fuzzy ART for producing the correct trend coming from the first level. The experiments show that the resulting network is capable of forecasting the changes in the input and identifying the trends correctly
Recommended Citation
B. Fulkerson et al., "A Hierarchial Neural Network Implementation for Forecasting," Proceedings of the IEEE International Conference on Neural Networks, 1994, Institute of Electrical and Electronics Engineers (IEEE), Jan 1994.
The definitive version is available at https://doi.org/10.1109/ICNN.1994.374744
Meeting Name
IEEE International Conference on Neural Networks, 1994
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
ART Neural Nets; Backpropagation; Forecasting Theory; Fuzzy Neural Nets; Gating Network; Hierarchical Neural Network Architecture; Maximum Likelihood Competitive Learning Algorithm; Maximum Likelihood Estimation; Multilayer Perceptrons; Time Series; Time Series Forecasting; Unsupervised Learning
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 1994 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jan 1994
