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George Tzagkarakis
Researcher at Foundation for Research & Technology – Hellas
Publications - 68
Citations - 757
George Tzagkarakis is an academic researcher from Foundation for Research & Technology – Hellas. The author has contributed to research in topics: Compressed sensing & Wireless sensor network. The author has an hindex of 13, co-authored 64 publications receiving 683 citations. Previous affiliations of George Tzagkarakis include DSM & French Alternative Energies and Atomic Energy Commission.
Papers
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Proceedings ArticleDOI
Anomaly Detection for Symbolic Time Series Representations of Reduced Dimensionality
TL;DR: In this paper, the authors proposed a framework for anomaly detection of streaming data in lower-dimensional spaces, utilizing a modification of the symbolic aggregate approximation for dimensionality reduction and a statistical hypothesis testing based on the Kullback-Leibler divergence.
Journal ArticleDOI
Exploiting Market Integration for Pure Alpha Investments via Probabilistic Principal Factors Analysis
TL;DR: In this article, a long-short beta neutral portfolio strategy is proposed based on earnings yields forecasts, where positions are modified by accounting for time-varying risk budgeting by employing an appropriate integration measure.
Journal ArticleDOI
Recurrence quantification analysis of denoised index returns via alpha-stable modeling of wavelet coefficients: Detecting switching volatility regimes
TL;DR: Results indicate an improved interpretation capability of RQA when applied on denoised data using the proposed approach, and an increased accuracy of the proposed method in detecting switching volatility regimes, which is important for estimating the risk associated with a financial instrument.
Posted Content
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly Detection.
TL;DR: In this paper, the authors proposed two data-driven SAX-based symbolic representations, distinguished by their discretization steps, based on the combination of kernel density estimation and Lloyd-Max quantization to minimize the information loss and mean squared error.
Proceedings ArticleDOI
Compressive video classification for decision systems with limited resources
George Tzagkarakis,Pavlos Charalampidis,Grigorios Tsagkatakis,Jean-Luc Starck,Panagiotis Tsakalides +4 more
TL;DR: The properties of linear random projections in the framework of compressive sensing are exploited to reduce the task of classifying a given video sequence into a problem of sparse reconstruction, based on feature vectors consisting of measurements lying in a low-dimensional compressed domain.