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• 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• 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,…