Hybridized Forecasting: Integrating LSTM And RNN with Traditional ARIMA Models

Abstract

The research presents a comparative investigation of both traditional statistical and modern deep learning models for Time Series forecasting. Specifically, it explores the performance of ARIMA (Auto Regressive Integrated Moving Average), ARIMAX (ARIMA with exogenous variables), LSTM (Long Short-Term Memory), and a Hybrid LSTM-RNN (Recurrent Neural Network) approach. The goal is to evaluate their effectiveness in modeling and predicting complex, seasonal, and nonlinear sales trends, using stock market data from Reliance Industries Limited (RIL). The datasets selected for this study are well-known for their strong seasonality and trend components, making them ideal for evaluating forecasting model performance. The analysis begins with exploratory data analysis and statistical diagnostics, including stationarity testing via the Augmented Dickey-Fuller test, autocorrelation and partial autocorrelation analyses, and Time Series decomposition into trend, seasonal, and residual components. These preliminary steps help shape the modeling approach and provide insights into long-term behavior patterns within the data.