Optimizing multilayer neural networks using fractal dimensions of time-series data

A fractal dimension of time-series data is used to optimize a three-layer feedback neural network which was proposed previously to detect an important time structure of time-series data and to predict a future sequence based on a current input sequence. Optimization means in a sense that the prediction error is minimized. A time interval giving the same fractal dimensions is used as an optimal size of the output layer. The number of input units is twice the number of output units. It is also found that reliability in prediction is determined empirically as a function of the fractal dimension