Predicting the Price of Stock Using Deep Learning Algorithms

In today's world, it is the best opportunity for an individual to beat the inflation effects and accumulate wealth for various life goals. But the problem comes down to the risks related to the stock market investment. As is known the stock market's nature is extremely unpredictable and volatile. So, stock market price prediction is an attempt in this domain to make investment a little safer and more reliable. The purpose of this initiative is to provide more trustworthy and accurate system for predicting the price activity of stocks. N-BEATS model is composed of a succession of stacks, each of which holds a large number of blocks. The local properties of the time series are the focus of each block's partial prediction. Before forwarding the result to the next stack, the incomplete predictions from the blocks it contains are combined in the stack. By evaluating the suggested prediction efficiency method in this research with that of other approaches, it might be argued that the advised method was successful in accurately predicting the stock price to a promising extent. The N-BEATS deep learning architecture's predictive capabilities were contrasted with that of a number of well-known time series forecasting techniques, including LSTM and ARIMA. The MAPE, MAE, and RMSE are used to calculate prediction errors and the accuracy of the models. When compared to LSTM and ARIMA models, the findings show that the created N-BEATS model has potential predictive performance and in terms of accuracy, the results reveal that N-BEATS surpasses the other two models.