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Linear versus Exponential Forecasting Techniques: A Comparative Study of ARIMA and Holt–Winters Triple Exponential Smoothing Models for Indian Retail Gold Prices - Evidence from daily retail gold price data in India, 2014–2025

Author(s) Rajib Bhattacharya
Country India
Abstract This study conducts a comparative evaluation of two classical time-series forecasting approaches—AutoRegressive Integrated Moving Average (ARIMA) and Holt–Winters Triple Exponential Smoothing (HW–TES)—to analyse and predict daily retail gold prices in India over the period 2014–2025. Gold, as both a cultural and financial asset, exhibits strong cyclical and seasonal dynamics, reflecting patterns driven by festive consumption, investment demand, and global macroeconomic shifts. The study aims to determine which framework—linear stochastic modelling or exponential adaptive smoothing—offers superior predictive performance and responsiveness under conditions of market volatility and structural change.
The methodology adopts the Box–Jenkins approach for ARIMA model identification and the multiplicative Holt–Winters formulation for capturing trend and seasonality. Both models are calibrated using daily 24-carat retail gold prices sourced from official data repositories. The comparative evaluation is based on accuracy indicators such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), supplemented by the Diebold–Mariano (DM) test to assess the statistical significance of forecasting differences.
Empirical results reveal that both models perform commendably, with MAPE values below 5%, confirming high short-term predictive reliability. However, the Holt–Winters model consistently demonstrates slightly lower forecast errors and greater adaptability to rapid price shifts, attributed to its exponential weighting mechanism that prioritizes recent observations. In contrast, ARIMA’s linear lag structure produces delayed adjustments to trend reversals, leading to mild underestimation during volatile phases.
The findings confirm that while ARIMA retains its value as a robust, interpretable statistical benchmark, Holt–Winters exponential smoothing offers superior short-run responsiveness and adaptability, making it particularly suitable for dynamic and seasonally driven markets like India’s retail gold sector. This study contributes to the broader forecasting literature by reaffirming that model selection should be context-dependent, guided by the temporal characteristics and behavioural complexity of financial time series.
Keywords Gold Price Forecasting, ARIMA, Holt–Winters, Time-Series Modelling, Seasonality JEL Classification: C22, C53, E37, G17, Q02
Published In Volume 6, Issue 10, October 2025
Published On 2025-10-31
Cite This Linear versus Exponential Forecasting Techniques: A Comparative Study of ARIMA and Holt–Winters Triple Exponential Smoothing Models for Indian Retail Gold Prices - Evidence from daily retail gold price data in India, 2014–2025 - Rajib Bhattacharya - IJLRP Volume 6, Issue 10, October 2025. DOI 10.70528/IJLRP.v6.i10.1814
DOI https://doi.org/10.70528/IJLRP.v6.i10.1814
Short DOI https://doi.org/g98nd6
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