Using ARIMA and LSTM to Implement Stock Market Analysis

There has been a lot of discussion about the rapid shift that is taking place in the global environment toward the adoption of online shopping on a massive scale as well as the previously unheard-of volatility of the global stock market. Both of these topics have received a lot of attention in recent times. Both of these subjects have generated a significant amount of debate recently. Making accurate predictions about the market has always been a cause of anxiety for investors. Investors have always been interested in finding a practical solution that could perfectly predict the price. This may be accomplished by having an awareness of how the market repeats itself or by analysing patterns that may assist achieve a higher return on investment. Investors have always been interested in discovering a solution that could accurately anticipate the price, regardless of the strategy that has been used. In this investigation, the methodologies of "Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Auto-Regressive Integrated Moving Average" are combined to create a one-of-a-kind hybrid model (ARIMA). This study analyses and predicts movements in the stock market by looking at a broad variety of technologies, including those that are presently in use. The overall objective of this research is to analyse and predict changes in the stock market. An investigation into the development of historical patterns was carried out with the assistance of the data collected from the companies whose stocks are traded on the National Stock Exchange (NSE). This investigation included a time span of six years and made use of the facts pertaining to the stock's previous close, open, high, and low values. We will utilise the historical trends that have occurred in the past so that we may analyse and forecast patterns that will occur in the days that are to come. This will be accomplished via the use of historical trends. Because of its capacity to process large datasets and do trend analysis, Python and R have attracted a lot of attention in recent years.