Time Series Modeling and Forecasting of Reliance Industries Stock Prices Using ARIMA and Statistical Decomposition Techniques

Author(s)	Dr. T Sudha, Ms. I Geetha
Country	India
Abstract	The current research carries out a comprehensive time-series analysis of the stock prices of Reliance Industries Ltd. (RELIANCE.NS) applying the classical statistical modeling techniques. The dataset constituted of historical daily closing prices is preprocessed, interpolated to a daily frequency, and subjected to a variety of tests including the ones for trend, seasonality, autocorrelation, and stationarity. Smoothed behavior is captured by moving averages. Sea sonal decomposition is done to pinpoint trend, seasonal, and residual factors. Autocorrelation (ACF) and partial autocorrelation (PACF) are investigated to discover ARIMA model parameter configuration. A fitted ARIMA(5,1,2) rollercoaster is the one that generates 30-day-long forecasts. Findings disclose that the stock is on a solid upward path with slight seasonal influences and easily predictable short-term autocorrelation. The stability and clarity of the ARIMA model facilitate short-term forecasting. This study not only emphasizes the potency of statistical methods in the arena of financial forecasting but also sets the path for integrating further sophisticated machine learning approaches.
Keywords	Time Series Analysis, ARIMA, RELIANCE.NS, Financial Forecasting, Deep Learning.
Field	Mathematics > Statistics
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-12-27
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.64706

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