Mendeley
TY  _  JOUR
ID  -  1401120841383
TI  -  Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms
JO  -  Journal of Information and Communication Technology
JA  -  ES
LA  -  fa
SN  -  2717-0411
AU  -  Safari dehnavi Vahid
AU  -  shafiee masoud
AD  -  صنعتی امیرکبیر
AD  -  استاد داشگاه صنعتی امیرکبیر
Y1  -  2023
PY  -  2023
VL  -  53
IS  -  
SP  -  149
EP  -  167
KW  -  Prediction
KW  -   optimized grasshopper optimization algorithm
KW  -   GMDH neural network
KW  -   Modeling
DO  -  
N2  -  Stock market prediction serves as an attractive and challenging field for researchers in financial markets. Many of the models used in stock market prediction are not able to predict accurately or these models require a large amount of input data, which increases the volume of networks and learning complexity, all of which ultimately reduce the accuracy of forecasting. This article proposes a method for forecasting the stock market that can effectively predict the stock market. In this paper, the past market price is used to reduce the volume of input data and this data is placed in a regressor model.
UR  - rimag.ir/en/Article/41383
L1  - rimag.ir/en/Article/Download/41383
ER  -  
