Volume 6, Issue 3, May 2013, Pages 473 - 486
Agile Prediction of Ongoing Temporal Sequences Based on Dominative Random Subsequences
- Ning Yang, Changjie Tang
- Corresponding Author
- Ning Yang
Available Online 9 January 2017.
- https://doi.org/10.1080/18756891.2013.781332How to use a DOI?
- Temporal sequence, Agile Prediction, Dominative Random Subsequence, Temporal similarity
- 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.
- 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 - 2017 DA - 2017/01 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 - https://doi.org/10.1080/18756891.2013.781332 ID - Yang2017 ER -