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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: [email protected]
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
Model assumptions
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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
Scheme of the algorithm
Model experiment 1: Single event
Model experiment 2: Two events
Approaching the Sun...
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,…