Lag-ℓ forecasting and machine-learning algorithms

Jae Joon Ahn, Il Suh Son, Kyong Joo Oh, Tae Yoon Kim, Gyu Moon Song

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this study, we discuss the problem of lag-ℓ forecasting, which has been solved using a machine-learning algorithm. The main aim of this study is to define the lag-ℓ forecaster based on a precise classification approach and to discuss the technical issues involved in the lag-ℓ forecasting problem, including a comparison of various machine-learning algorithms for proper implementation of the technique. This study focuses on an application that uses the lag-ℓ forecaster in an early-warning system.

Original languageEnglish
Pages (from-to)269-282
Number of pages14
JournalExpert Systems
Volume28
Issue number3
DOIs
Publication statusPublished - 2011 Jul 1

Fingerprint

Learning algorithms
Learning systems
Forecasting
Learning Algorithm
Machine Learning
Early Warning
Alarm systems

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Theoretical Computer Science
  • Computational Theory and Mathematics

Cite this

Ahn, Jae Joon ; Son, Il Suh ; Oh, Kyong Joo ; Kim, Tae Yoon ; Song, Gyu Moon. / Lag-ℓ forecasting and machine-learning algorithms. In: Expert Systems. 2011 ; Vol. 28, No. 3. pp. 269-282.
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Lag-ℓ forecasting and machine-learning algorithms. / Ahn, Jae Joon; Son, Il Suh; Oh, Kyong Joo; Kim, Tae Yoon; Song, Gyu Moon.

In: Expert Systems, Vol. 28, No. 3, 01.07.2011, p. 269-282.

Research output: Contribution to journalArticle

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