Open AccessForecasting the Price of Fuel Oil: A STL-(ELM+ARIMA) Combination Approach
Author(s) -
Fang Yu,
Yanqing Liu,
Chenxi Zhang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1903/1/012048
Subject(s) - autoregressive integrated moving average , parametric statistics , series (stratigraphy) , computer science , oil price , decomposition , time series , econometrics , machine learning , economics , mathematics , statistics , paleontology , ecology , monetary economics , biology
Focusing on fuel oil price forecasting, we propose a “decomposition-prediction-integration” route and STL-(ELM+ARIMA) combination forecasting model. This model decomposes the fuel oil price time series by STL, and effectively combines the advantages of high frequency seasonal cycle and short-term fluctuation time series forecasting in ELM non-parametric model with the advantages of low frequency trend forecasting in ARIMA parametric model. Finally, this paper conducts an empirical study on the spot price of Singapore’s Platts fuel oil 180CST to verify the effectiveness of the proposed forecasting method. The results show that the forecasting accuracy of 180 CST fuel oil price model based on STL-( ELM (1) + ARIMA (2) +ELM (3)) is highest.