Crude Oil Price Forecasting Using Hybrid Heuristic Model and Fuzzy C-Means on Type 2 Fuzzy Time Series

Abstract

Abstract : Forecasting world crude oil prices needs to be done because it has an essential role in Indonesia’s economy, so an accurate and efficient forecasting approach is needed. This research combines heuristic and Fuzzy C-Means (FCM) models on Type 2 Fuzzy Time Series (T2FTS) to forecast crude oil prices. T2FTS, an extension of Fuzzy Time Series (FTS) by adding observations, is used to enrich the fuzzy relationship of the Type 1 model and can improve forecasting performance. FCM is used to determine unequal interval lengths and heuristic models to optimize fuzzy relations by identifying crude oil price movements using up and down trends. The data used in this study is the price of Brent crude oil as one of the benchmarks for world crude oil prices from 1 January 2023 – 31 May 2024. Mean Absolute Percentage Error (MAPE) measures accuracy in assessing forecasting results. The results showed that the combination of heuristic and FCM models in T2FTS gave accurate results, as evidenced by the MAPE value obtained, which was 1.50%, so it fell into the excellent category.