Stock Market Prediction using RFR, DTR & SVR

Stock market or equity market have a profound impact in today's economy. An increase or fall within the share worth has a very important role in deciding the investor's gain. the prevailing foretelling strategies build use of each regression (AR, MA, ARIMA) and non-linear algorithms (ARCH, GARCH, Neural Networks), but they concentrate on predicting the indicator movement or worth foretelling for one company victimization the daily price. The planned methodology could be a model freelance approach. Here we have a tendency to don't seem to be fitting the info to a particular model, rather we have a tendency to be distinguishing the latent dynamics existing within the information victimization deep learning architectures. During this work we have a tendency to use 3 totally different deep learning architectures for the value prediction of firms and compares their performance. We have a tendency to be applying a window approach for predicting future values on a brief term basis. The performance of the models were quantified victimization proportion error.