Exploring Time-Series Forecasting Model for Accurate Dynamic Stock Price Prediction Using Facebook Prophet

EasyChair Preprint no. 13206

Abstract

The stock market is volatile in nature and accurate prediction of Stock price is a challenge task for the researchers. In literature, current stock forecasting methods rely on a limited set of variables. To enhance forecasting capabilities, factorslike open price,close price, high price, low price, Return on Equity (ROE), Return on Capital Equity (ROCE), daily return, and trading volume are integrated in the present forecasting framework. Dynamic data techniques are used to extract real-time data from leading financial websites and forecasting stock prices for next four years using Facebooks Prophet algorithm, which has been implemented in Streamlit. The experimental results show better accuracy and a low error rate.

Keyphrases: dynamic data, Prophet, stock price, Streamlit

@Booklet{EasyChair:13206,
  author = {Amrutha Sai Kopparthi and Asritha Vrunda Kopparthi and Geetha Anusha Penumarthi and Pavan Kumar Kolluru},
  title = {Exploring Time-Series Forecasting Model for Accurate Dynamic Stock Price Prediction Using Facebook Prophet},
  howpublished = {EasyChair Preprint no. 13206},

  year = {EasyChair, 2024}}