International Journal of Computational Intelligence Systems

Volume 6, Issue 3, May 2013, Pages 473 - 486

Agile Prediction of Ongoing Temporal Sequences Based on Dominative Random Subsequences

Authors
Ning Yang, Changjie Tang
Corresponding Author
Ning Yang
Available Online 1 May 2013.
DOI
https://doi.org/10.1080/18756891.2013.781332How to use a DOI?
Keywords
Temporal sequence, Agile Prediction, Dominative Random Subsequence, Temporal similarity
Abstract
This paper identifies a new paradigm of prediction, of ongoing temporal sequences, which achieves an acceptable accuracy just by the historical subsequences as short as possible and as close to the predicted time point as possible. To address agile prediction, a new concept, (DRS for short), is first introduced to capture the local influence and local regularity of the subsequences that are decisive to the future of an ongoing temporal sequence. DRS mining algorithm, MDRS, and its optimal implementation OptMDRS, are also presented. In MDRS and OptMDRS, DRSs are organized as a suffix tree, DRS-Tree, to facilitate the retrieval. Next, this paper proposes an agile prediction algorithm, AgilePredict, to make accurate predictions based the DRS that is closest to the predicted time point. Finally, the results of the extensive experiments conducted on synthetic and real data sets show that our proposed method is feasible and efficient for agile prediction.
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This is an open access article distributed under the CC BY-NC license.

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
6 - 3
Pages
473 - 486
Publication Date
2013/05
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.1080/18756891.2013.781332How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - JOUR
AU  - Ning Yang
AU  - Changjie Tang
PY  - 2013
DA  - 2013/05
TI  - Agile Prediction of Ongoing Temporal Sequences Based on Dominative Random Subsequences
JO  - International Journal of Computational Intelligence Systems
SP  - 473
EP  - 486
VL  - 6
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.781332
DO  - https://doi.org/10.1080/18756891.2013.781332
ID  - Yang2013
ER  -