Agile Prediction of Ongoing Temporal Sequences Based on Dominative Random Subsequences

DOI

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Ning Yang
AU  - Changjie Tang
PY  - 2013
DA  - 2013/05/01
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  - 10.1080/18756891.2013.781332
ID  - Yang2013
ER  -