Signal2Vec: Time Series Embedding Representation. Nalmpantis, C. & Vrakas, D. In pages 80–90. May, 2019. doi abstract bibtex 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.
@incollection{nalmpantis_signal2vec_2019,
title = {{Signal2Vec}: {Time} {Series} {Embedding} {Representation}},
isbn = {978-3-642-54671-6},
shorttitle = {{Signal2Vec}},
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.},
author = {Nalmpantis, Christoforos and Vrakas, Dimitris},
month = may,
year = {2019},
doi = {10.1007/978-3-030-20257-6_7},
pages = {80--90},
}
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