<![CDATA[The decline and fluctuations in natural resource production in East Kalimantan, particularly oil, natural gas, and coal, create uncertainty regarding future production conditions, necessitating forecasting methods suited to the characteristics of time-series data. However, few studies have specifically compared Holt’s Linear Trend Method, Brown’s Double Exponential Smoothing (Brown’s DES), and the Theta Model using East Kalimantan natural resource data with a relatively limited number of annual observations. This study aims to compare the performance of these three methods in forecasting oil lifting, natural gas lifting, and coal production. The novelty of this study lies in its comparative evaluation of the three methods across three natural resource indicators with different time-series patterns, as well as its assessment of their ability to use limited historical data to generate five-year projections. The Theta Model was included as a comparative method because it can represent time-series patterns through trend decomposition and extrapolation, making it relevant for evaluating data that exhibit long-term changes. The study uses annual data for the 2016–2024 period obtained from the Statistics Indonesia Office of East Kalimantan Province and the East Kalimantan One Data portal. The use of annual data with a relatively limited number of observations was considered when selecting the methods because trend- and smoothing-based approaches such as Holt, Brown’s DES, and Theta can be applied to time series with limited sample sizes without requiring the estimation of a large number of parameters. The research stages included data collection, model development using the three forecasting methods, accuracy evaluation based on Mean Absolute Percentage Error (MAPE), and five-year forecasting. The results show that the Theta Model performed best for oil and natural gas, with MAPE values of 13.04% and 17.42%, respectively. Meanwhile, Brown’s DES produced the lowest MAPE for coal, at 37.35%. The relatively high MAPE values for coal indicate that the three methods have not fully captured the characteristics of the data's fluctuations, and no single forecasting method consistently outperforms the others across all natural resource indicators; therefore, the selection of a method must take into account the pattern characteristics and levels of variation inherent in each time series. The limited accuracy of coal forecasting highlights the need to explore alternative methods capable of capturing fluctuating patterns.]]>
