Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2022.2043762
Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm
Yan Chen
Dongxu Mo
Feipeng Zhang
Sažetak
Evolutionary computation and data mining are two fascinating
fields that have attracted many researchers. This paper proposes
a new rule mining method, named genetic network programming
(GNP), to solve the prediction problem using the evolutionary
algorithm. Compared with the conventional association rule methods
that do not consider the weight factor, the proposed algorithm
provides many advantages in financial prediction, since it
can discover relationships among the attributes of different transactions.
Experimental results on data from the New York
Exchange Market show that the new method outperforms other
conventional models in terms of both accuracy and profitability,
and the proposed method can establish more important and
accurate rules than the conventional methods. The results confirmed
the effectiveness of the proposed data mining method in
financial prediction.
Ključne riječi
Evolutionary computations; decision analysis; association rule mining; genetic network programming; stock movement prediction
Hrčak ID:
302959
URI
Datum izdavanja:
31.3.2023.
Posjeta: 160 *