Analysis of Private Sector Bank Index Prices Using Arima Model

Abstract

One of the most important financial assumptions that investors face is and will always be stock price forecasting. There are many approaches to accurately predict a company's share price, most of which depend on different variables that affect the share price in the market. Time series data analysis is one of the main models used in data analysis. A significant class of models in machine learning, econometrics, and statistics is time series forecasting. Predictions from a time series model are typically predicated on the idea that historical trends will recur in the future. This paper establish comparison of four Arima (1,0,1), (5,1,1) (1,1,1) & (4,1,2) modal for private sector bank index from 10/06/24 to 20/06/24. The result shows that (5,1,1) gives the best result.