A Best Algorithm for Short-Term and Long-Term Stock Price Prediction

Stock price forecasting may be a popular and important topic in financial and academic studies. Share A market is an untidy place for predicting since there are not any significant rules to estimate or predict the price of shares within the share market. Many methods like technical analysis, fundamental analysis, statistic analysis, and statistical analysis, etc. are all wont to plan to predict the worth within the share market but none of those methods are proved as a consistently acceptable prediction tool. In this project, we attempt to implement different algorithms (Moving Average, Linear Regression, KNN, ARIMA, LSTM) to predict stock market prices and will check which algorithm gives us the best prediction. Artificial Neural networks are very effectively implemented in forecasting stock prices, returns, and stock modeling, and therefore the most frequent methodology. We select a certain group of parameters with a relatively significant impact on the share price of a company. With the assistance of statistical analysis, the relation between the chosen factors and share price is formulated which may help in forecasting accurate results. Although the share market can never be predicted, due to its vague dons, this project aims at applying different Time Series Analysis Algorithm and Non-Time series Analysis Algorithm in forecasting the stock prices.