A. Thammano and S. Palahan (Thailand)
Time Series, Forecasting, Fuzzy System, Artificial Neural
Network, Genetic Algorithm, Hybrid System
This paper proposes a new time series forecasting system,
whose learning algorithm is a hybrid of the fuzzy c-means
algorithm, the genetic algorithm, and the backpropagation
algorithm. The proposed fuzzy-neural system consists of 5
layers: the input layer, the fuzzification layer, the rule
layer, the hidden layer, and the output layer. The fuzzy c
means algorithm is used to determine the center and width
of the fuzzy membership functions. The artificial neural
network is used as the fuzzy inference engine, while the
genetic algorithm is used to optimize the fuzzy rule-base.
This proposed system is tested with five time series data.
The results obtained are very encouraging.