Inflation Forecasting Using ARIMA and Its Implications for Online Pricing Strategies in Tanjungpinang City

Siti Arita  Novia; Salsabila; Leni Anggaraini  Susanti

doi:10.36456/jstat.vol19.no1.a11394

Authors

Siti Arita Novia Raja Ali Haji Maritime University
Salsabila Makassar State University
Leni Anggaraini Susanti Bali State Polytechnic

DOI:

https://doi.org/10.36456/jstat.vol19.no1.a11394

Keywords:

ARIMA, Forecasting, Inflation, Pricing Strategies

Abstract

This study examines the role of inflation forecasting as a supporting input for online pricing strategy formulation in the context of digital businesses in Tanjungpinang. Using monthly inflation data from January 2008 to October 2025 published by the Indonesian Central Bureau of Statistics (BPS), the analysis applies a seasonal ARIMA(0,0,1)(2,0,0)[12] model to generate inflation projections for the period November 2025 to October 2027. Diagnostic evaluations, including residual analysis and goodness-of-fit assessment, confirm that the selected model adequately captures the underlying data structure. The forecasting results indicate that inflation in Tanjungpinang is expected to remain low and relatively stable over the two-year horizon, with moderate and controlled fluctuations ranging from 0.17% to 0.42% per month. Based on these empirical findings, the study then presents a conceptual and literature-based analysis of how the forecast results can inform online pricing strategies, including dynamic pricing, promotional pricing, competitive pricing, cost-based pricing, and algorithmic pricing. It is important to note that the pricing strategy component of this study is interpretive and conceptual in nature, drawing on relevant literature rather than constituting an empirical test of pricing behavior. The analysis highlights that stable inflation conditions allow firms to prioritize demand-driven adjustments, competitive positioning, and margin optimization rather than frequent cost-driven repricing. By integrating regional inflation projections with conceptual pricing considerations in digital markets, this research contributes to the literature on data-driven pricing and offers practical insights for digital enterprises and policymakers.

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