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Neural Network Approach to Discovering Temporal Correlations

Explore a cutting-edge algorithm for discovering causal relationships between behaviors and events through temporal correlations. By analyzing data sequences of scene images, this model aims to predict geomagnetic storms and other phenomena with unknown feature combinations. The research focuses on finding the most probable phenomenon and delay between events of different types, enhancing forecasting accuracy and reliability. Collaborative competition among neural network experts is proposed to further develop the algorithm for broader applications in seismology, medicine, finance, and beyond.

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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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