Prediksi Harga Minyak Dunia Dengan Metode Deep Learning
DOI:
https://doi.org/10.21111/fij.v6i1.4446Keywords:
Abstract
AbstrakPeramalan seri waktu mendapatkan banyak perhatian dari berbagai penelitian. Salah satu data seri waktu yang barubah setiap periode tertentu adalah minyak bumi. Secara umum harga minyak bumi dipengarui oleh dua hal yaitu permintaan dan pendapatan. Pada penelitian ini menggunakan state-of-the-art model Deep Learning LSTM (Long Short Term Memory) untuk meramalkan harga minyak dalam periode tertentu. Metode ini digunakan karena arsitekturnya dapat beradaptasi dengan belajar non-linear dari data seri waktu yang kompleks. Dataset yang digunakan adalah data Brent Oil Price yang selalu di update setiap minggu. Dataset ini berisi harga minyak brent dari tahun 1987 sampai sekarang. Beberapa model yang dibangun terbukti dapat meramalkan harga minyak dengan baik. Model terbaik yang didapatkan dari penelitian ini memiliki RMSE 0,0186 dan MAE 0,013.Kata kunci: LSTM, deep learning, peramalan, harga minyak Abstract[Forecasting World Oil Price with Deep Learning Method] Time series forecasting gets a lot of attention from various studies. One of the time-series data that changes every certain period is petroleum. In general, the price of petroleum is affected by two things, namely demand and income. This research uses a state-of-the-art Deep Learning LSTM (Long Short-Term Memory) model to predict the oil price in a certain period. This method is used because the architecture can adapt to non-linear learning from complex time series data. The dataset used is the Brent Oil Price data, which is always updated every week. This dataset contains the price of Brent oil from 1987 to the present. The models that were built proved to be able to predict oil prices well. The best models obtained from this study have RMSE 0.0186 and MAE 0.013.Keywords: LSTM, deep learning, forecasting, oil priceReferences
[1] P. Ponadi, D. Amboningtyas, and A. Fathoni, “ANALYSIS OF THE EFFECT OF WORLD OIL PRICE INCREASE, AMOUNT OF CIRCULAR MONEY, AND EXCHANGE ON INFLATION IN INDONESIA (Case Study of Mining Companies in the 2013-2017 Period),” J. Manage., vol. 5, no. 5, 2019.[2] A. Sagheer and M. Kotb, “Time series forecasting of petroleum production using deep LSTM recurrent networks,” Neurocomputing, vol. 323, pp. 203–213, 2019.[3] M. R. Mahdiani and E. Khamehchi, “A modified neural network model for predicting the crude oil price,” Intellect. Econ., vol. 10, no. 2, pp. 71–77, 2016, doi: 10.1016/j.intele.2017.02.001.[4] J. Wang and J. Wang, “Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations,” Energy, vol. 102, pp. 365–374, 2016.[5] L. Yu, X. Zhang, and S. Wang, “Assessing potentiality of support vector machine method in crude oil price forecasting,” Eurasia J. Math. Sci. Technol. Educ., vol. 13, no. 12, pp. 7893–7904, 2017, doi: 10.12973/ejmste/77926.[6] M. Abusalah, “Brent Oil Price,” 2020. https://www.kaggle.com/mabusalah/brent-oil-prices (accessed Mar. 20, 2020).[7] S. Jain, S. Shukla, and R. Wadhvani, “Dynamic selection of normalization techniques using data complexity measures,” Expert Syst. Appl., vol. 106, pp. 252–262, 2018.[8] Scikit-learn.org, “6.3. Preprocessing Data — Scikit-Learn 0.23.1 Documentation,” 2020. https://scikit-learn.org/stable/modules/preprocessing.html (accessed Mar. 23, 2020).[9] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.[10] Y. Sudriani, I. Ridwansyah, and H. A. Rustini, “Long short term memory (LSTM) recurrent neural network (RNN) for discharge level prediction and forecast in Cimandiri river, Indonesia,” in IOP Conference Series: Earth and Environmental Science, 2019, vol. 299, no. 1, p. 12037.[11] A. G. Salman, Y. Heryadi, E. Abdurahman, and W. Suparta, “Single layer & multi-layer long short-term memory (LSTM) model with intermediate variables for weather forecasting,” Procedia Comput. Sci., vol. 135, pp. 89–98, 2018.[12] V. K. R. Chimmula and L. Zhang, “Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks,” Chaos, Solitons & Fractals, p. 109864, 2020.[13] Y. Ding, Y. Zhu, J. Feng, P. Zhang, and Z. Cheng, “Interpretable Spatio-Temporal Attention LSTM Model for Flood Forecasting,” Neurocomputing, 2020.[14] S. Muzaffar and A. Afshari, “Short-term load forecasts using LSTM networks,” Energy Procedia, vol. 158, pp. 2922–2927, 2019.[15] M. Zaheer, S. Reddi, D. Sachan, S. Kale, and S. Kumar, “Adaptive methods for nonconvex optimization,” in Advances in neural information processing systems, 2018, pp. 9793–9803.
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