Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices

Ariyanti, Vika Putri; Yusnitasari, Tristyanti

doi:10.29207/resti.v7i2.4895

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…Then, the forecasting results for the next seven days with ARIMA are 86.230003 while SARIMA is 86.260002, so ARIMA is better than SARIMA. The results of this study are expected to help policymakers make the right policies and decisions in using crude oil [21].…”

Section: Introductionmentioning

confidence: 94%

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Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods

Santoso,

Widodo

2024

SISFOKOM

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Forests are an area and part of the environmental cycle that is very important for survival because forests are areas on Earth that regulate the balance of the ecosystem. Forest fires rank second only to illegal logging in Indonesia's list of forest destruction causes. Forest fires can occur due to two factors, namely natural and human factors. Therefore, the hotspot factor that can cause forest fires is an independent variable. The population of hotspots in the West Kalimantan region in 2020 amounted to 1,416 spots. This study aims to predict the number of hotspot occurrences on land and forests that cause fires before the fires spread and are challenging to overcome or extinguish. The method to indicate the number of hotspot occurrences uses the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) methods. Modeling ARIMA (0,1,1) and SARIMA (0,1,1) (2,2,1)12 obtained Root Mean Square Error (RMSE) evaluation results for ARIMA of 6.61 while SARIMA of 7.61. The ARIMA's Mean Squared Error (MSE) evaluation value is 43.70, and the SARIMA is 58.05. Based on these results, it can be concluded that the ARIMA model provides excellent and accurate performance in describing the trend of hotspot events that will occur in the future with a smaller RMSE value compared to SARIMA.

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Section: Introductionmentioning

confidence: 94%

“…Hotspots are indicators of forest fires that detect an area with a relatively higher temperature than the surrounding temperature [10]. Hotspots are points on the image (pixels or sub-pixels) that have very high temperatures and are associated with active Earth surface fires [21].…”

Section: B Hotspotmentioning

confidence: 99%

Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods

Santoso,

Widodo

2024

SISFOKOM

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“…Similarly, "q" is commonly employed to represent the order of moving average [7]. Utilising techniques such as time series stationarity, parameter estimation, model verification, and prediction, the ARIMA model was created [8].…”

Section: Arima Model Settingmentioning

confidence: 99%

The Impact of Soaring Crude Oil Prices on the Air Transportation Industry Index

Wang

2023

HBEM

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On February 24, 2022, Russian President Vladimir Putin cited "demilitarisation and de-Nazification" as a justification for military intervention, leading to the deployment of Russian forces to invade Ukraine. In addition, it is important to note that Russia and Ukraine, as prominent global oil-exporting regions, annually contribute approximately 200 million tonnes of crude oil to the global market. The conflict between these two countries has significantly influence the global crude oil prices, thereby exerting a significant influence on businesses that rely heavily on energy resources. As one of the industries with the highest energy demands, the aviation industry is also substantially affected. Examining the relationship between energy costs and the airline industry, this paper will examine fundamental concepts such as the impact of fuel prices on the aviation industry, role of fuel cost in operational costs, and the effects of fuel cost volatility on airline stock. In order to determine the effects of fuel cost fluctuations the global air transport industry, the theoretical framework for this essay including comprised of pertinent theories, Demand and Supply theory, the Perfect Competition Model, and the Market Efficiency Hypothesis. This paper argues that the rise in crude oil prices caused by the conflict between Ukraine and Russia has a considerable negative impact on the stock price of the air transportation industry.

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“…Penelitian selanjutnya dilakukan oleh Vika Putri Ariyanti dan Tristyani Yusnitasari pada tahun 2023 dengan melakukan perbandingan metode antara ARIMA dan SARIMA dalam memprediksi harga minyak dunia. Hasil menunjukkan bahwa kedua model memiliki nilai RMSE yang sama sebesar 0.01905% [17]. Penelitian selanjutnya yang diteliti oleh Jhon Veri, Surmayanti, dan Guslendra pada tahun 2020 menggunakan algoritma jaringan syaraf tiruan (JST) untuk memprediksi harga minyak dunia.…”

Section: Pendahuluanunclassified

Penerapan Deep Learning Menggunakan Gated Recurrent Unit Untuk Memprediksi Harga Minyak Mentah Dunia

Saputra,

Insani,

Agustian

et al. 2023

bits

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Crude oil is a much-needed energy for the whole world. Each country is inseparable from the use of crude oil for use in various sectors, such as transportation, so that the price of world crude oil is the most important variable for the world. Fluctuations in oil prices will cause various problems, such as inflation, changes in market prices, and others. Therefore, the prediction of world crude oil prices is very important as a consideration for decision making. This study implements deep learning using the Gated Recurrent unit model. The data used is the price of Brent crude oil with a total of 5834 data, starting from January 4, 2000 to December 19, 2022. The parameters used are the number of GRU units, batch size, and lookback. The best model produced in this study is the GRU model with hyperparameters consisting of 30 lookbacks, 50 GRU units, and 256 batch sizes with the lowest MAPE value among the other models, which is 2.25%. The MAPE value states that predictions using the GRU model are said to be very good at predicting world crude oil prices

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Comparison of ARIMA and SARIMA for Forecasting Crude Oil Prices

Cited by 7 publications

References 16 publications

Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods

Predicting the Number of Forest and Land Fire Hotspot Occurrences Using the ARIMA and SARIMA Methods

The Impact of Soaring Crude Oil Prices on the Air Transportation Industry Index

Penerapan Deep Learning Menggunakan Gated Recurrent Unit Untuk Memprediksi Harga Minyak Mentah Dunia

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