Stock Index Probability Prediction using the FB Prophet Model

Given how unpredictable the stock market has recently become, it is now crucial to be able to accurately predict the future trend of equities. Because of the financial crisis, it is now essential to estimate stock values accurately in order to make money. Non-linear signal forecasting requires sophisticated statistical models and machine learning methods. We must identify the stock’s purchasing and selling points for a specific stock market history in order to make a profit. The Dow Theory, MACD, the Relative Strength Index (RSI), the Exponential Moving Average, and others have all been used to forecast stock market performance. It could be challenging to make a specific judgement because these models could provide a variety of indications. The aforementioned issue was attempted to be solved by a wave of machine learning algorithms in artificial intelligence. Some strategies have yet to provide promising results, while others have not reacted as well on the stock market exchange, due to the non-linear structure of the stock market signals. We intend to employ the Prophet model, which is reliable and generates findings with minimal data. The Prophet refers to the method of forecasting time series data based on an additive model with non-linear trends suited with holiday impacts, daily seasonality, weekly, and yearly. Strong seasonal impacts in time-series data are ideal for it. The prophet is resilient to missing data and adept at handling outliers. Facebook has made available the prophet, an open-source Python package. Predictions of stock prices are shown by indicators like RSI, MACD, Super Trend and others.