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Neural Network Approach to Discovering Temporal Correlations

Neural Network Approach to Discovering Temporal Correlations. S.A.Dolenko, Yu.V.Orlov, I.G.Persiantsev, Ju.S.Shugai Scobeltsyn Institute of Nuclear Physics, Moscow State University E-mail: yvo@radio-msu.net. Statement of the problem.

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Neural Network Approach to Discovering Temporal Correlations

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  1. Neural Network Approach to Discovering Temporal Correlations S.A.Dolenko, Yu.V.Orlov, I.G.Persiantsev, Ju.S.ShugaiScobeltsyn Institute of Nuclear Physics,Moscow State UniversityE-mail: yvo@radio-msu.net

  2. Statement of the problem • Discovering causal relationship “behavior - event” - What type of behavior has initiated the event? - What phenomenon has initiated the event? • Application - geomagnetic storms forecasting; SOHO -http://sohowww.nasacom.nasa.gov • Complexity of the task - What is the delay between the event and the moment of its initiation? - Can use “passive observation” only Objective of the research: Development of an algorithm for discovering temporary correlations

  3. Model assumptions • Data = Sequence of scene images • Scene = Set of objects • Lifetime of objects >> Registration rate • Object = Set of features • Phenomenon = Unknown combination of features • Event: - Initiated by unknown phenomenon within “Initiation duration” - Search interval >> Initiation duration - Limited number of events’ types - Fixed (unknown) delay for a given type of event Find the most probable phenomenon and delay

  4. Scheme of the algorithm

  5. Model experiment 1: Single event

  6. Model experiment 2: Two events

  7. Approaching the Sun...

  8. Future development • NN experts specialization through competition • Second hierarchical level - NN Supervisor • Discovering temporal correlations “Sun surface - Geomagnetic storms”- Increasing forecast horizon- Improving forecast reliability • Applications in seismology, medicine, finance,…

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