Strategic Integration for Future Selection-LSTM Stock Prediction Algorithm based on the Internet of Things (IoT)

Forecasting stock prices is essential because of its substantial ramifications for people, organizations, and governments. This research investigates the convergence of sophisticated data mining methods, 5G technology, and the Internet of Things (IoT) to improve the accuracy of predicting stock prices. To overcome the difficulties associated with decreased accuracy and intricate training procedures in stock prediction models, to suggest using a hybrid methodology that integrates Feature Selection (FS) and Long Short-Term Memory (LSTM) networks. This approach seeks to forecast the final price of stocks by using an enhanced collection of characteristics derived from 15 commonly used indicators in the stock market. By using feature selection (FS), we decrease the number of data dimensions and enhance the efficiency of model training. Subsequently, the LSTM model is used to predict stock values. The findings of the research indicate that the FS-LSTM hybrid model substantially enhances the accuracy of predictions, resulting in a notable decrease of 15% in mean squared error compared to conventional LSTM networks. These results highlight the possibility of combining IoT and 5G technology in financial forecasting, which may lead to more dependable and efficient predictions of stock prices.