LEVERAGING THE SIGNIFICANT INPUTS/DATA BASED ON LONG AND SHORT TERM MEMORY TO ENHANCE THE EFFECTIVE PREDICTION OF STOCK MARKET

Gaurav Chhikara

Volume 2, Issue 1 2018

Page: 25-30

Abstract

A model that will use an LSTM model to predict stock prices is proposed in this study. Will use information from the past to predict stock prices. Because it can learn long-term dependencies in data, stacked LSTM is an ideal method for stock market prediction due to its dynamic and complex nature. This means that the predictions will be more accurate because it uses historical data. The model's accuracy will be checked using test data after it has been trained, and then the model will use this model to forecast stock prices for the next 30 days.

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