Title :
Learning Long-Term Time Series with Generative Topographic Mapping
Author_Institution :
Fairchild Semicond., South Portland
Abstract :
We propose a generative topographic mapping (GTM) based nonlinear model for long-term time series prediction. As a modification of Kohonen self-organizing maps (SOM), GTM has been applied to data classification, visualization and other machine learning problems, however, limited research have been proposed in time series analysis. With a double application of GTM algorithm, a specially designed approach can quantize input data to store temporal evolvement information for trend prediction. Experimental results demonstrate the improved forecast accuracy in long-term trend learning.
Keywords :
data analysis; pattern classification; prediction theory; self-organising feature maps; time series; unsupervised learning; Kohonen self-organizing map; data classification; data visualization; generative topographic mapping-based nonlinear model; long-term time series prediction; machine learning; unsupervised classification algorithm; Autoregressive processes; Data mining; Data visualization; Economic forecasting; Machine learning; Machine learning algorithms; Neural networks; Predictive models; Self organizing feature maps; Time series analysis;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4370947
