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International Conference on Engineering Applications of Neural Networks

EANN 2019: Engineering Applications of Neural Networks pp 80–90Cite as

Signal2Vec: Time Series Embedding Representation

Part of the Communications in Computer and Information Science book series (CCIS,volume 1000)

Abstract

The rise of Internet-of-Things (IoT) and the exponential increase of devices using sensors, has lead to an increasing interest in data mining of time series. In this context, several representation methods have been proposed. Signal2vec is a novel framework, which can represent any time-series in a vector space. It is unsupervised, computationally efficient, scalable and generic. The framework is evaluated via a theoretical analysis and real world applications, with a focus on energy data. The experimental results are compared against a baseline using raw data and two other popular representations, SAX and PAA. Signal2vec is superior not only in terms of performance, but also in efficiency, due to dimensionality reduction.

Keywords

  • Time series
  • Data mining
  • Representations
  • Time series classification
  • Energy embeddings
  • Non intrusive load monitoring

This work has been funded by the \(\mathrm{E}\Sigma \Pi \mathrm{A}\) (2014–2020) Erevno-Dimiourgo-Kainotomo 2018/EPAnEK Program ’Energy Controlling Voice Enabled Intelligent Smart Home Ecosystem’, General Secretariat for Research and Technology, Ministry of Education, Research and Religious Affairs.

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

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References

  1. Asgari, E., Mofrad, M.R.: Continuous distributed representation of biological sequences for deep proteomics and genomics. PloS one 10(11), e0141287 (2015)

    CrossRef  Google Scholar 

  2. Barkan, O., Koenigstein, N.: Item2Vec: neural item embedding for collaborative filtering. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6. IEEE (2016)

    Google Scholar 

  3. Batra, N., et al.: NILMTK: an open source toolkit for non-intrusive load monitoring. In: Proceedings of the 5th International Conference on Future Energy Systems, pp. 265–276. ACM (2014)

    Google Scholar 

  4. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)

    Google Scholar 

  5. Chan, K.P., Fu, A.W.C.: Efficient time series matching by wavelets. In: Proceedings of the 15th International Conference on Data Engineering, 1999, pp. 126–133. IEEE (1999)

    Google Scholar 

  6. Chen, Q., Chen, L., Lian, X., Liu, Y., Yu, J.X.: Indexable PLA for efficient similarity search. In: Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 435–446. VLDB Endowment (2007)

    Google Scholar 

  7. Corduas, M., Piccolo, D.: Time series clustering and classification by the autoregressive metric. Comput. Stat. Data Anal. 52(4), 1860–1872 (2008)

    MathSciNet  CrossRef  Google Scholar 

  8. Egarter, D., Pöchacker, M., Elmenreich, W.: Complexity of power draws for load disaggregation (2015). arXiv preprint arXiv:1501.02954

  9. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases, vol. 23. ACM (1994)

    Google Scholar 

  10. Garcia-Duran, A., Bordes, A., Usunier, N.: Composing relationships with translations. Ph.D. thesis, CNRS, Heudiasyc (2015)

    Google Scholar 

  11. Gutmann, M.U., Hyvärinen, A.: Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res. 13(Feb), 307–361 (2012)

    MathSciNet  MATH  Google Scholar 

  12. Kelly, J., Knottenbelt, W.: The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2, 150007 (2015)

    CrossRef  Google Scholar 

  13. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Sigmod Rec. 30(2), 151–162 (2001)

    CrossRef  Google Scholar 

  14. Keogh, E.J., Pazzani, M.J.: A simple dimensionality reduction technique for fast similarity search in large time series databases. In: Terano, T., Liu, H., Chen, A.L.P. (eds.) PAKDD 2000. LNCS (LNAI), vol. 1805, pp. 122–133. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45571-X_14

    CrossRef  Google Scholar 

  15. Korn, F., Jagadish, H.V., Faloutsos, C.: Efficiently supporting ad hoc queries in large datasets of time sequences. In: ACM Sigmod Record, vol. 26, pp. 289–300. ACM (1997)

    Google Scholar 

  16. Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Disc. 15(2), 107–144 (2007)

    MathSciNet  CrossRef  Google Scholar 

  17. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013). CoRR abs/1301.3781. http://arxiv.org/abs/1301.3781

  18. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  19. Minnen, D., Isbell, C.L., Essa, I., Starner, T.: Discovering multivariate motifs using subsequence density estimation and greedy mixture learning. In: Proceedings of the National Conference on Artificial Intelligence, vol. 22, p. 615. AAAI Press; MIT Press, Menlo Park, Cambridge, London (1999, 2007)

    Google Scholar 

  20. Nalmpantis, C., Krystalakos, O., Vrakas, D.: Energy profile representation in vector space. In: 10th Hellenic Conference on Artificial Intelligence SETN 2018. ACM (2018)

    Google Scholar 

  21. Nalmpantis, C., Vrakas, D.: Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif. Intell. Rev. 1–27 (2018)

    Google Scholar 

  22. Ozsoy, M.G.: From word embeddings to item recommendation (2016). arXiv preprint arXiv:1601.01356

  23. Portet, F., et al.: Automatic generation of textual summaries from neonatal intensive care data. Artif. Intell. 173(7–8), 789–816 (2009)

    CrossRef  Google Scholar 

  24. Ratanamahatana, C., Keogh, E., Bagnall, A.J., Lonardi, S.: A novel bit level time series representation with implication of similarity search and clustering. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 771–777. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_90

    CrossRef  Google Scholar 

  25. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    CrossRef  Google Scholar 

  26. Wu, L., Fisch, A., Chopra, S., Adams, K., Bordes, A., Weston, J.: Starspace: Embed all the things (2017)! arXiv preprint arXiv:1709.03856

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Authors and Affiliations

  1. School of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Christoforos Nalmpantis & Dimitris Vrakas

Authors
  1. Christoforos Nalmpantis
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  2. Dimitris Vrakas
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Corresponding author

Correspondence to Christoforos Nalmpantis .

Editor information

Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John Macintyre

  2. Democritus University of Thrace, Xanthi, Greece

    Prof. Lazaros Iliadis

  3. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  4. Oxford Brookes University, Oxford, UK

    Chrisina Jayne

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Nalmpantis, C., Vrakas, D. (2019). Signal2Vec: Time Series Embedding Representation. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_7

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  • DOI: https://doi.org/10.1007/978-3-030-20257-6_7

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