文档介绍：STRUCTURE LEARNING OF WORKS FROM
DATABASES BY IC ALGORITHMS
APPLICATION TO TIME SERIES PREDICTION IN FINANCE
Jérôme HABRANT
Ecole Nationale Supérieure des Mines de St Etienne – 158, Cours Fauriel – 42023 St Etienne Cedex – France
(******@)
Key words: ic algorithms, works, Learning from a database of cases
Abstract: This paper outlines a ic algorithm based method for constructing works from databases.
Our method permits the generation of plete structure if there is no expert for the domain studied. Also
it allows taking advantage of the knowledge about the domain by specifying connections in work. To
test our method, we applied it to time series prediction in finance with 5 shares. We experimented 3 different
ic algorithms: first, we used classical syntactical ic operators, second we add 2 high-level ic
operators by taking the semantic of the structures into consideration, and third, we add a last powerful
operator. Furthermore, we studied 3 constraints on the structures: by assuming an ordering between the
nodes, by releasing the ordering assumption and by forcing the structures to use all available information to
build the forecasts. For each of the 3 ic algorithms and the 3 constraints, we present our results
concerning the ic algorithms convergence and the predictive power of the best structures obtained. Our
results are encouraging.