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2015 | 5 | 381--388
Tytuł artykułu

Robust Histogram-based Feature Engineering of Time Series Data

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Collecting data at regular time nowadays is ubiquitous. The most widely used type of data that is being collected and analyzed is financial data and sensor readings. Various businesses have realized that financial time series analysis is a powerful analytical tool that can lead to competitive advantages. Likewise, sensor networks generate time series and if they are properly analyzed can give a better understanding of the processes that are being monitored. In this paper we propose a novel generic histogram-based method for feature engineering of time series data. The preprocessing phase consists of several steps: deseansonalyzing the time series data, modeling the speed of change with first derivatives, and finally calculating histograms. By doing all of those steps the goal is three-fold: achieve invariance to different factors, good modeling of the data and preform significant feature reduction. This method was applied to the AAIA Data Mining Competition 2015, which was concerned with recognition of activities carried out by firefighters by analyzing body sensor network readings. By doing that we were able to score the third place with predictive accuracy of about 83%, which was about 1% worse than the winning solution.(original abstract)
Słowa kluczowe
Rocznik
Tom
5
Strony
381--388
Opis fizyczny
Twórcy
  • Faculty of Computer Science and Engineering Ss.Cyril and Methodius University, Skopje, Macedonia
  • Faculty of Computer Science and Engineering Ss.Cyril and Methodius University, Skopje, Macedonia
autor
  • Intelligent Technologies, Negotino, Macedonia
  • Faculty of Computer Science and Engineering Ss.Cyril and Methodius University, Skopje, Macedonia
  • Faculty of Computer Science and Engineering Ss.Cyril and Methodius University, Skopje, Macedonia
Bibliografia
  • ] S. Movassaghi, M. Abolhasan, J. Lipman, D. Smith, and A. Jamalipour, "Wireless body area networks: A survey," pp. 1658-1686, Third 2014.
  • T. chung Fu, "A review on time series data mining," Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 164 - 181, 2011. doi: http://dx.doi.org/10.1016/j.engappai.2010.09.007. [Online]. Available: http://www.sciencedirect.com/science/ article/pii/S0952197610001727
  • P. Esling and C. Agon, "Time-series data mining," ACM Comput. Surv., vol. 45, no. 1, pp. 12:1-12:34, Dec. 2012. doi: 10.1145/2379776.2379788. [Online]. Available: http://doi.acm.org/10.1145/2379776.2379788
  • B. Hu, Y. Chen, and E. Keogh, "Classification of streaming time series under more realistic assumptions," Data Mining and Knowledge Discovery, pp. 1-35, 2015. doi: 10.1007/s10618-015-0415-0. [Online]. Available: http://dx.doi.org/10.1007/s10618-015-0415-0
  • M. Meina, A. Janusz, K. Rykaczewski, D. Slezak, B. Celmer, and A. Krasuski, "Tagging firefighter activities at the emergency scene: Summary of AAIA'15 data mining competition at Knowledge Pit," in Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2015, in print September 2015.
  • A. Krasuski, "A framework for dynamic analytical risk management at the emergency scene. from tribal to top down in the risk management maturity model," in Computer Science and Information Systems (FedCSIS), 2014 Federated Conference on. IEEE, 2014, pp. 323- 330.
  • A. Krasuski, A. Jankowski, A. Skowron, and D. Slezak, "From sensory data to decision making: A perspective on supporting a fire commander," in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2013, pp. 229-236.
  • M. Meina, B. Celmer, and K. Rykaczewski, "Towards robust framework for on-line human activity reporting using accelerometer readings," in Active Media Technology. Springer, 2014, pp. 347-358.
  • "ICRA' project," http://www.icra-project.org/, accessed: 2015-06-05.
  • A. G. Barnett and A. J. Dobson, Analysing Seasonal Health Data. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2010. ISBN 9783642107481 3642107486
  • R. Agrawal, C. Faloutsos, and A. N. Swami, "Efficient similarity search in sequence databases," in Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms. Springer-Verlag, 1993, pp. 69-84.
  • I. Jolliffe, "Principal Component Analysis," in Wiley StatsRef: Statistics Reference Online, N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, and J. L. Teugels, Eds. Chichester, UK: John Wiley & Sons, Ltd, Sep. 2014. ISBN 9781118445112. [Online]. Available: http://doi.wiley.com/10.1002/9781118445112.stat06472
  • C.-C. Chang and C.-J. Lin, "Libsvm: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 27:1-27:27, May 2011. doi: 10.1145/1961189.1961199. [Online]. Available: http://doi.acm.org/10.1145/1961189.1961199
  • D. M. Tax and R. P. Duin, "Feature scaling in support vector data descriptions," Technical report, Technical report, American Association for Artificial Intelligence, Tech. Rep., 2000.
  • C.-W. Hsu, C.-C. Chang, C.-J. Lin et al., "A practical guide to support vector classification."
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171422212

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