Abstract:

Studying the geoelectric precursory data streams, a new timeseries data stream data model based on the Sequence MultiIndex Successive Trees (SMIST) is put forward, which uses index to create SMIST through scanning the sequence by one time, and then uses index matching and pattern growth to generate frequent patterns. The results in both theory analyses and experiments show that this algorithm is so simple and direct, efficient, and has a practical value. In order to discover the hidden regularity of the geoelectric knowledge, a large number of geoelectric precursory data streams are  processed and analyzed. The newlyfound regular patterns and the trend of geoelectric parameters can provide a basis for detecting abnormal precursors, which can be used in earthquake prediction.

Key words: multiindex successive tree;geoelectric precursors;timeseries data stream;frequent pattern