A Time-Dependent Enhanced Support Vector Machine For Time ...
A Time-Dependent Enhanced Support Vector Machine For Time ...
A Time-Dependent Enhanced Support Vector Machine For Time ...
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[7] H. Cheng, P. N. Tan, C. Potter, and S. Klooster.<br />
Detection and characterization of anomalies in<br />
multivariate time series. In Proceedings of SDM, 2009.<br />
[8] Y. Cheung and L. Xu. Independent component<br />
ordering in ica time series analysis. Neurocomputing,<br />
41(1-4):145–154, 2001.<br />
[9] B. R. Dai, J. W. Huang, M. Y. Yeh, and M. S. Chen.<br />
Adaptive clustering for multiple evolving streams.<br />
IEEE Transactions on Knowledge & Data<br />
Engineering, 18(9):1166–1180, 2006.<br />
[10] DataMarket. http://datamarket.com/, 2011.<br />
[11] R. Drossu and Z. Obradovic. Rapid design of neural<br />
networks for time series prediction. IEEE<br />
Computational Science And Engineering, 3(2), 1996.<br />
[12] S. Dzeroski, V. Gjorgjioski, I. Slavkov, and J. Struyf.<br />
Analysis of time series data with predictive clustering<br />
trees. Knowledge Discovery in Inductive Databases,<br />
4747, 2007.<br />
[13] C. Freudenthaler, S. Rendle and L. Schmidt-Thieme.<br />
Factorizing markov models for categorical time series<br />
prediction. In Proceedings of ICNAAM, pages<br />
405–409, 2011.<br />
[14] A. W. Fu, E. Keogh, L. Y. Lau, C. A.<br />
Ratanamahatana, and R. Wong. Scaling and time<br />
warping in time series querying. VLDB Journal,<br />
17(4):899–921, 2008.<br />
[15] S. Greco, M. Ruffolo, and A. Tagarelli. Effective and<br />
efficient similarity search in time series. In Proceedings<br />
of the 15th ACM CIKM, pages 808 – 809, 2006.<br />
[16] Q. He, K. Chang, and E. P. Lim. Analyzing feature<br />
trajectories for event detection. In Proceedings of the<br />
30th Annual ACM SIGIR, pages 207–214, 2007.<br />
[17] K. Huarng and T. H. Yu. The application of neural<br />
networks to forecast fuzzy time series. Physica A:<br />
Statistical Mechanics and its Applications,<br />
363(2):481–491, 2006.<br />
[18] Y. Kawahara and M. Sugiyama. Change-point<br />
detection in time-series data by direct density-ratio<br />
estimation. In Proceedings of SDM, 2009.<br />
[20] Y. Li, J. Lin, and T. Oates. Visualizing<br />
variable-length time series motifs. In SIAM<br />
Conference on Data Mining (SDM), 2012.<br />
[21] W. Liu and S. Chawla. A quadratic mean based<br />
supervised learning model for managing data<br />
skewness. In Eleventh SDM, pages 188–198, 2011.<br />
[22] X. Liu, X. Wu, H. Wang, R. Zhang, J. Bailey, and<br />
K. Ramamohanarao. Mining distribution change in<br />
stock order streams. In IEEE ICDE, pages 105–108,<br />
2010.<br />
[23] Y. Liu, M. T. Bahadori, and H. Li. Sparse-GEV:<br />
Sparse latent space model for multivariate extreme<br />
value time series modelling. In Proceedings of ICML,<br />
2012.<br />
[24] A. D. McQuarrie and C. L. Tsai. Outlier detections in<br />
autoregressive models. Journal of Computational and<br />
Graphical Statistics, 12(12):450–471, 2003.<br />
[25] A. Mueen and E. Keogh. Online discovery and<br />
maintenance of time series motifs. In Proceedings of<br />
the 16th ACM SIGKDD, pages 1089–1098, 2010.<br />
[26] C. H. Teo, S. V. N. Vishwanthan, A. J. Smola, and<br />
Q. V. Le. Bundle methods for regularized risk<br />
minimization. Journal of <strong>Machine</strong> Learning Research,<br />
11:311–365, 2010.<br />
[27] R. S. Tsay. Outliers, level shifts, and variance changes<br />
in time series. Journal of forecasting, 7(1):1–20, 1988.<br />
[28] V. S. Tseng, C. H. Chen, P. C. Huang, and T. P.<br />
Hong. Cluster-based genetic segmentation of time<br />
series with dwt. Pattern Recognition Letters,<br />
30(13):1190–1197, 2009.<br />
[29] Wessa. http://www.wessa.net/stocksdata.wasp, 2011.<br />
[30] Z. Xing, J. Pei, P. S. Yu, and K. Wang. Extracting<br />
interpretable features for early classification on time<br />
series. In Proceedings of SDM, 2011.<br />
[31] H. Yang, L. Chan, and I. King. <strong>Support</strong> vector<br />
machine regression for volatile stock market<br />
prediction. Intelligent Data Engineering and<br />
Automated Learning - IDEAL, 2412:391–396, 2002.<br />
[19] N. Khoa and S. Chawla. Robust outlier detection<br />
using commute time and eigenspace embedding.<br />
Advances in Knowledge Discovery and Data Mining,<br />
6119:422–434, 2010.