Magh Heryan Tudaan, Aris Pransicco Siringo Ringo

Comparative Analysis of Univariate Time Series Models in Forecasting Premium Rice Prices in East Kalimantan

Introduction

Comparative analysis of univariate time series models in forecasting premium rice prices in east kalimantan. Compare ARIMA, DES, & Trend Projection for forecasting premium rice prices in East Kalimantan (2018-2024). DES proves most accurate (5.59% MAPE), aiding food security & economic stability.

Abstract

This study aims to forecast the price of premium rice in East Kalimantan for the period 2018 to 2024 using a univariate time series approach. The main issue addressed is the fluctuation in rice prices, which can affect household economic stability and regional food security. In this context, the accuracy of rice price predictions is crucial to support decision-making by stakeholders in the agricultural and trade sectors. Three forecasting methods analyzed in this study include Autoregressive Integrated Moving Average (ARIMA), Double Exponential Smoothing (DES), and Trend Projection (TP). The performance of these methods was evaluated using the Mean Absolute Percentage Error (MAPE) as a measure of accuracy. The results show that the DES method provides the highest accuracy, with a MAPE value of 5.59%, compared to 7.85% for ARIMA and 16.87% for TP. The best ARIMA model obtained was ARIMA (2,1,0), but it was still less precise than DES. These findings also reveal a tendency for premium rice prices to increase over the study period, influenced by external factors such as changes in economic conditions and supply chain disruptions. Therefore, DES is identified as the most reliable method for short-term price trend forecasting. This result contributes significantly to strategic planning in facing volatile market conditions and supports efforts to stabilize rice prices as part of regional food security initiatives.

Review

This study presents a timely and relevant analysis concerning the crucial issue of premium rice price forecasting in East Kalimantan, an endeavor with direct implications for regional food security and household economic stability. The authors address a critical need for accurate price predictions to inform strategic decision-making within the agricultural and trade sectors. By undertaking a comparative analysis of three prominent univariate time series models—ARIMA, Double Exponential Smoothing (DES), and Trend Projection (TP)—the research aims to identify the most robust method for short-term price trend forecasting for the period 2018-2024. The structured approach to model comparison and performance evaluation, utilizing MAPE, is a commendable aspect of this work. The methodology employed meticulously evaluates the performance of the selected models, providing a clear comparison of their predictive power. The findings clearly demonstrate the superior accuracy of the Double Exponential Smoothing (DES) method, achieving a Mean Absolute Percentage Error (MAPE) of 5.59%. This contrasts favorably with ARIMA, which yielded a MAPE of 7.85% (with the optimal model identified as ARIMA (2,1,0)), and significantly outperforms Trend Projection's 16.87% MAPE. This clear identification of DES as the most reliable method for short-term forecasting is a significant practical contribution. Furthermore, the study notes an underlying upward trend in premium rice prices, attributed to broader economic shifts and supply chain disruptions, reinforcing the necessity for precise forecasting to manage market volatility. The identification of DES as the most accurate model provides a concrete and practical tool for stakeholders in East Kalimantan to anticipate short-term fluctuations in premium rice prices, thereby bolstering efforts to stabilize markets and enhance food security. This result significantly contributes to strategic planning in facing volatile market conditions, fulfilling the study's aim of supporting decision-making. While the focus on univariate models provides a strong foundational analysis, future research could potentially explore the incorporation of relevant multivariate factors to potentially enhance prediction accuracy and offer deeper explanatory power for long-term trends. Nevertheless, this study makes a valuable contribution by offering a practical, data-driven approach to a critical regional challenge, laying a solid foundation for more informed policy and strategic planning in a volatile agricultural market.

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