Stock Price Analysis and Prediction using Facebook Prophet Model with Python


  • Loveleen Kumar Assistant Professor, Global Institute of Technology, Jaipur, Rajasthan, India.
  • Shivam Soni UG Scholar, Global Institute of Technology, Jaipur, Rajasthan, India.


Time Series Data, Stock Market, Stock Prediction, FB Prophet, Python


The stock market has always been attracting people with its highreturns but with it comes challenges and risks. A stock exchange markedepicts savings and investments that are advantageous to increase the effectiveness of the national economy. The future stock returns have some predictive relationships with the publicly available information of present and historical stock market indices. FB PROPHET is a statistical model which is known to be efficient for time series forecastingespecially for short-term prediction. In this paper, we propose a modelfor forecasting the stock market trends based on the technical analysis
using historical stock market data and FB PROPHET model. This model will automate the process of predicting the direction of future stock price indices, thereby, helping the financial specialists choose better timing for purchasing and/or selling of stocks. The results are shown in terms of web-application as front end and using Python programming language. The obtained results reveal that the FB PROPHET model has a strong potential for short-term prediction of stock market trends.

How to cite this article:
Kumar L, Soni S, Sharma S et al. Stock Price Analysis and Prediction using Facebook Prophet Model with Python. J Adv Res Model Simul 2020; 2(2): 15-17.


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