By Topic

Multivariate time series prediction via temporal classification

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
$31 $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

2 Author(s)
Bing Liu ; Sch. of Comput., Nat. Univ. of Singapore, Singapore ; Jing Liu

In this paper, we study a special form of time-series prediction, viz. the prediction of a dependent variable taking discrete values. Although in a real application this variable may take numeric values, the users are usually only interested in its value ranges, e.g. normal or abnormal, not its actual values. In this work, we extended two traditional classification techniques, namely the naive Bayesian classifier and decision trees, to suit temporal prediction. This results in two new techniques: a temporal naive Bayesian (T-NB) model and a temporal decision tree (T-DT). T-NB and T-DT have been tested on seven real-life data sets from an oil refinery. Experimental results show that they perform very accurate predictions

Published in:

Data Engineering, 2002. Proceedings. 18th International Conference on

Date of Conference: