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N-gram Events for Analysis of Financial Time Series

  • Igor BorovikovEmail author
  • Michael Sadovsky
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Discretization of time series and encoding it as a string in a finite alphabet allows application of information theory methods developed for discrete signals. Computing information values of n-grams extracted from such string leads to introduction of events as occurrences of n-grams that possess specific properties, e.g. abnormally high (or low) information value. We define information value of an n-gram via maximum entropy lifts over frequency dictionaries. We also look for correlation between market events and n-gram events. The paper shows that the proposed method of time series analysis when applied to events study may provide new insightful perspective.

Keywords

Input Text Market Event Information Capacity Maximum Entropy Principle Financial Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Nekkar.net: Int. LabsFoster CityUSA
  2. 2.Institute of Computational Modelling SB RASKrasnoyarskRussia

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