Stock Price Prediction using LSTM and KNN Algorithms

Abstract

Abstract: The aim of this study is to enhance and evaluate the performance of Long Short-Term Memory (LSTM) and K Nearest Neighbor (KNN) algorithms when used to forecast stock prices. In this study, previously recorded data on price as well as other crucial factors like transaction volume and market sentiment must be collected from reliable sources. The information gathered is then cleaned, preprocessed and refined in order to make sure it is compatible with the training model. Then, we developed two forecasting models, one using (LSTM) and (KNN). LSTM models are trained using historical data, and the performance of these models is evaluated using a variety of metrics, such as mean squared error and mean error. The performance evaluation and comparison of the above two models are conducted saying LSTM model outperforms the KNN model in accuracy.