Education, Science, Technology, Innovation and Life
Open Access
Sign In

A Fast Time Series Rule Finding Based on Motif Searching

Download as PDF

DOI: 10.23977/poweet.2017.11006 | Downloads: 16 | Views: 1781

Author(s)

Tingting Dou 1, Haizhou Du 1, Yuchen Mao 2, Shaohua Zhang 1

Affiliation(s)

1 School of Computer Science and Technology, Shanghai University of Electric Power
2 School of Energy and Mechanical Engineering, Shanghai University of Electric Power

Corresponding Author

Tingting Dou

ABSTRACT

With the rapid economic development, people's demand for control of pollution emissions more and more intense. Thermal power plants must find ways to keep units running economically and efficiently, meet the minimum energy efficiency and emission standards and meet the environmental requirements. So we propose the algorithm of fast time series rule finding based on motif searching in this paper. We can use it to find what the reason is to achieve the optimal conditions of thermal power plants. What's more, the optimal time for the power plant units can be longer, the cost of the plant will be lower, and the goal of energy saving and emission reduction can be achieved. It has a guiding significance on the thermal power plant energy conservation and cost increasing.

KEYWORDS

Thermal power plants, Time series rule, Motif, Energy conservation.

CITE THIS PAPER

Tingting,D. , Haizhou, D. , Yuchen, M. , Shaohua, Z. A Fast Time Series Rule Finding Based on Motif Searching. International Journal of Power Engineering and Engineering Thermophysics (2017) 1: 35-39.

REFERENCES

[1] Shokoohi-Yekta, M., Chen, Y., Campana, B., Hu, B., Zakaria, J., Keogh, E. Discovery of Meaningful Rules in Time Series. In proceedings of KDD 2015.
[2] Weiss, S., Indurkhya, N., and Apte, C., Predictive Rule Discovery from Electronic Health Records. ACM IHI, 2010.
[3] Mueen, A., Keogh, E., Zhu, Q., Cash, S. and Westover, B. Exact Discovery of Time Series Motif. SDM 2009.
[4] Abonyi, J., Feil, B., Nemeth, S., Arva, P. Modified GathCGeva clustering for fuzzing segmentation of multivariate time series. Fuzzy Sets and Systems, Data Mining Special Issue 149, 2005: 39 - 56.
[5] Tak-chung Fu. A review on time series data mining. In: Proceedings of Engineering Applications of Artificial Intelligence, 2011: 164 - 181.
[6] Hu, B., et al. Discovering the Intrinsic Cardinality and Dimensionality of Time Series Using MDL. ICDM 2011.
[7] G. E. A. P. A. Batista, X. Wang, E. J. Keogh, A Complexity-Invariant Distance Measure for Time Series, SDM, 2011: 699 - 710.
[8] E. J. Keogh, J. Lin, S. H. Lee, and H V. Herle. Finding the most unusual time series subsequence: algorithms and applications, Knowl. Inf. Syst., vol 11, no. 1, 2007: 1 - 27.
[9] K. Ueno, X. Xi, E. J. Keogh, D. J. Lee, Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining, ICDM, 2006: 623 - 632.
[10] D. Yankov, E. J. Keogh, U. Rebbapragada, Disk aware discord discovery: finding unusual time series in terabyte sized datasets, In proceedings of Knowl. Inf. Syst., vol 17, no. 2, 2008: 241 - 262.
[11] Rakthanmanon,T., Keogh, E., Lonardi,S,. MDL-Based Time Series Clustering. In proceedings of Knowledge and Information Systems. 2012, Volume 33, Issue 2: 371 - 399.
[12] Rakthanmanon, T., Keogh, E., Lonardi, S., Evans, S.: MDL-based time series clustering. Knowl. Inf. Syst. 33(2), 2012: 371- 399.
[13] Vinh, V.T., Anh, D.T.: Some novel improvements for MDL-based semi-supervised classification of time series. In: Proceedings of Computational Collective Intelligence. Technologies and Applications, Springer, Berlin. LNAI 8733, 2014: 483 - 493.
[14] Begum, N., Hu, B., Rakthanmanon, T., Keogh, E. A minimum description length technique for semi-supervised time series classification. In: Integration of Reusable Systems Advances in Intelligent Systems and Computing, 2014: 171 - 192.
[15] Vinh, V.T., Anh, D.T.: Constraint-based MDL principle for semi-supervised classification of time series. In: Proceedings of 7th International Conference on Knowledge and System Engineering, Ho Chi Minh City, 8 - 10 Oct 2015: 43 - 48.

Downloads: 114
Visits: 14243

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.