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Intraday stock bid and its prediction have always been the cause of attraction for investors those who are interested in investing in the share market because of their getting immediate financial benefits from it within a little period of time. The main objective of conducting this study was to initiate an Autoregressive Forecasting model to predict the intra-day stock prices like a low price, close price, and high price. As such the investor is able to get the ideas of the above-said prices in order to get maximum financial benefits or there is the least chance of losing his invested amount. For our experimental purposes, we resorted to the dataset of ITC which is a very popular stock in the Indian stock market. For comparative research, we have taken four machine learning algorithms i.e Linear Regression, KNN regression, and Lasso regression. During our research, we compared each algorithm with the same dataset for finding the performance among them. Using the performance metrics i.e RMSE we have found out the best-fitted algorithm for our model. In consequence, we found that Lasso regression performed the best among all. |
