Time series prediction using neural networks



Journal Title

Journal ISSN

Volume Title


Texas Tech University


This thesis involves the investigation of the effect of prior knowledge embedded in an artificial fully connected recurrent neural network for the prediction of non-linear time series. The networks utilize the back propagation method for training. Two network architectures are compared using time series such as the square wave, Mackey Glass data, and an ECG signal to determine if prediction quality or training ability are improved when more information through cosine oscillators are embedded in the network. The benefit of such an exercise may be the prediction of abnormal ECG signals, which is an electrical measure of heart activity. Such ability would allow medical professionals to intervene and possibly prevent abnormal ECG signals. The improved network was able to provide increased prediction value and training ability for the Mackey Glass time series.



Prediction theory, Electrocardiography -- Data processing, Neural networks (Computer science), Time-series analysis -- Mathematical models