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Activity Number: 615
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: ENAR
Abstract #320373
Title: Geometrical and Symbolic Transformation of Sequential Data
Author(s): Rainhard Bengez*
Keywords: Time Series ; Sequential Data ; Fractal Geometry ; Cyber Trust ; Bifurcation

In data intensive research applying correlation based algorithms, Bayesian approaches, and neuronal networks (e.g. deep learning) seem to be the gold standard of mathematical and statistical methods. In the proposed talk I would like to introduce a different way of gaining information from high frequency time series data by a symbolic and geometrical transformation. For sure, sequential data can be analyzed in multiple ways. My proposed approach is framing sets of data points and associating them to a contraction mapping, i.e. the procedure consists mainly of the following steps: a) Clustering (or boxing) data points of a temporarily frozen time period b) Associating a contraction map to each cluster c) Varying the clusters (which might lead to complex forms and complex dynamic). d) Each specific cluster and associated transition mapping parses a characteristic function which can be plotted and investigated.

The properties of the characteristic function can be used to investigate high frequency time series data. This will be demonstrated on a given high frequency data sample from cyber defense and/or systems reliability.

Authors who are presenting talks have a * after their name.

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