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Signal2Vec: Time Series Embedding Representation

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Engineering Applications of Neural Networks (EANN 2019)

Part of the book series: Communications in Computer and Information Science ((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.

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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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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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

    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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20256-9

  • Online ISBN: 978-3-030-20257-6

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