Incorporating background knowledge for better prediction of cycle phases

Authors:
Ursula Garczarek;Claus Weihs
Affiliations:
Department of Statistics, University of Dortmund, Germany;Department of Statistics, University of Dortmund, Germany
Venue:
Knowledge and Information Systems
Year:
2004

Citing 5
Cited 0

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Fundamentals of speech recognition

Fundamentals of speech recognition
The nature of statistical learning theory

The nature of statistical learning theory
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.01

Visualization

Abstract

When predicting the state of a system, we sometimes know that the succession of states is cyclic. This is for example true for the prediction of business cycle phases, where an upswing is always followed by upper turning points and the subsequent downswing passes via lower turning points over to the next upswing and so on. We present several ideas of how to implement this background knowledge in popular static classification methods. Additionally, we present a full dynamic model. The usefulness for the prediction of business cycles is investigated, revealing pitfalls and potential benefits of ideas.