By Topic

The connectionist approach to multivariables forecasting of precipitation with virtual term generation schemes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Jo, T.C. ; Samsung SDS, Seoul, South Korea

Time series prediction is the prediction of future measurements by analyzing the relation among past values and a current observation. Many papers propose the neural approach to this instead of statistical approaches because neural network outperforms the statistical methods in time series prediction. If the neural approach replaces the statistical ones, it requires sufficient data for training. This paper proposes the schemes to generate artificially more data by estimating X(t+0.5), based on interpolation. The data for the experiments in this paper is about the precipitation of the three areas, east, middle, and west, in State Tennessee of the USA. The prediction performance is improved by more than 60% using the virtual term generation

Published in:

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on  (Volume:3 )

Date of Conference:

4-9 May 1998