Temporal data classification using linear classifiers

Authors:
Peter Revesz;Thomas Triplet
Affiliations:
Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, United States;Department of Computer Science, Concordia University, Montreal, Quebec, Canada H3G 1M8
Venue:
Information Systems
Year:
2011

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Abstract

Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality and on influenza using the Google Flu Trends database.