Stock market prediction using optimized grasshopper optimization algorithm and time series algorithms

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.