1. 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

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,…