A Time Series Analysis-Based Stock Price Prediction Framework Using Artificial Intelligence

Forecasting stock prices have recently emerged as an essential component of the economic realm. Stock price forecasting is regarded as a challenging endeavor due to the volatility and noise of stock market activity. In many stock price prediction scenarios, the Facebook Prophet, LightGBM and ARIMAX models have been demonstrated to be competitive versus other models. This research presents an architecture based on a time series model, such as Facebook Prophet, Light Gradient Boost Machine (GBM), and Autoregressive Integrated Moving Average with Explanatory Variable (ARIMAX) to accurately predict stock prices. Experiments with multiple potential outcomes are conducted to evaluate the suggested framework using the stock price data set. The model was trained on ADANI stock price data over the previous fourteen years using Facebook Prophet, LightGBM and ARIMAX and evaluated using the Root Mean Square Error metric (RMSE).