基於長短期記憶神經網路之航空油價預測

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姓名

林奕全(Yi-Chuan Lin) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

基於長短期記憶神經網路之航空油價預測
(Forecasting Aviation Fuel Prices Based on Long Short-Term Memory Neural Networks)

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至系統瀏覽論文 (2029-7-1以後開放)

摘要(中)

民用航空運輸業在全球化經濟體系中扮演重要角色，促進經貿往來及商務旅遊發展。近期面臨國際間戰爭、貿易紛爭擴大及全球供應鏈體系重組等挑戰，導致金融市場、股匯市及能源供需劇烈波動。燃油支出為各航空業者最大營運成本，有效預測航空油價為成本評估與航線效益決策之關鍵。本研究旨在開發航空燃油價格預測模型，探討四種不同時間序列預測方法，包括單變量、多變量、多步驟及遞迴預測模型。使用航空燃油價格數據，結合三大原油市場、標普500指數及美元指數，預測技術比較整合移動平均自迴歸模型和四種深度學習循環神經網路架構包括簡單循環神經網路、長短期記憶網路、閘門循環單元及雙向長短期記憶網路並運用網格搜索方法找到最佳超參數配置強化模型表現。研究結果顯示基於LSTM的深度學習預測模型在各種預測方法中表現皆優於ARIMA模型，在預測次週油價時，GRU模型的平均絕對百分比誤差(MAPE)為3.54%，而在預測次月油價時，雙向LSTM模型的MAPE為7.57%。多變量預測效能略低於單變量預測，原因為多重序列間的特徵權重未優化。在長期預測方面，本研究模型預測未來一年航空油價的誤差較預算油價基準平均降低7.97%，證明LSTM模型在短期預測中表現出色，在長期預測上具有發展潛力。

摘要(英)

The civil aviation transport industry plays a significant role in the globalized economic system, facilitating trade and business travel. Recently, it has faced challenges such as international conflicts, trade disputes, and the reorganization of global supply chain, leading to fluctuations in financial markets, stock and exchange rates, and energy supply and demand. Fuel expenses constitute the largest operating cost for airlines, making effective fuel price prediction crucial. This study aims to develop an aviation fuel price prediction model, using four time series forecasting methods: univariate, multivariate, multi-step, and recursive models. The research utilizes historical fuel price data combined with data from major crude oil markets, the S&P 500 index, and the US dollar index. The predictive techniques integrate the Autoregressive Integrated Moving Average model with four deep learning recurrent neural network architectures： Simple Recurrent Neural Network, Long Short-Term Memory, Gated Recurrent Unit, and Bidirectional LSTM, using grid search to find the optimal hyperparameter configuration. The results indicate that the LSTM-based prediction models outperform the ARIMA model across various forecasting methods. For next-week price prediction, the GRU model achieves a Mean Absolute Percentage Error of 3.54%, while for next-month prediction, the BiLSTM model achieves a MAPE of 7.57%. The performance of multivariate prediction is slightly weaker than that of univariate prediction, due to the suboptimal feature weighting among multiple sequences. In long-term forecasting, the proposed model reduces the error in predicting the next year’s fuel prices by an average of 7.97% compared to budgeted price benchmarks, demonstrating the LSTM model′s excellent performance in short-term prediction and its potential for long-term forecasting.

關鍵字(中)

★ 航空油價預測
★ 長短期記憶神經網路
★ 時間序列
★ 深度學習

關鍵字(英)

★ Aviation Fuel Price Forecasting
★ Long Short-Term Memory Networks
★ Time Series
★ Deep Learning

論文目次

摘要 I
ABSTRACT II
致謝 III
目錄 IV
表目錄 VI
圖目錄 VII
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
第二章文獻探討 5
2.1 油價對航空業的影響 5
2.2 時間序列相關研究 5
2.3 總結 10
第三章研究方法 11
3.1 資料蒐集和實驗欄位定義 12
3.1.1 資料蒐集 12
3.1.2 實驗欄位定義 14
3.2 時間序列預測技術 17
3.2.1 簡單循環神經網路 (Simple RNN) 17
3.2.2 長短期記憶網路 (LSTM) 18
3.2.3 閘門循環單元 (Gated Recurrent Unit) 19
3.2.4 雙向長短期記憶網路 (Bidirectional LSTM) 20
3.2.5 整合移動平均自迴歸模型 (ARIMA) 21
3.2.6 深度學習超參數 (Hyperparameter) 21
3.2.7 時間序列遞迴預測 (Recursive Forecasting) 22
3.3 實驗設計 23
3.4 評估指標 26
第四章實驗結果與分析 27
4.1 實驗結果 29
4.1.1 實驗一：單變量預測 30
4.1.2 實驗二：多變量預測 35
4.1.3 實驗三：多時間步驟預測 37
4.1.4 實驗四：預算基礎預測比較 42
4.2 綜合討論 43
第五章結論與建議 44
5.1 研究結論與貢獻 44
5.2 研究限制 45
5.3 未來研究方向 46
參考文獻 47
中文部分 47
英文部分 47

參考文獻

1.呂旻娟. (2013). 國際原油價格與美元匯率變動對國籍航空公司財務績效影響之研究 [碩士論文, 國立臺北大學]. 臺灣博碩士論文知識加值系統. 新北市.
2.李培煜. (2021). 時間序列ARIMA與深度學習LSTM預測模型之比較：以台灣股票市場為例 [碩士論文, 東吳大學]. 臺灣博碩士論文知識加值系統. 台北市.
3.莊國誠. (2022). LSTM模型對國立大專院校用電量進行預測–以國立陽明交通大學光復校區為例 [碩士論文, 國立陽明交通大學]. 臺灣博碩士論文知識加值系統. 新竹市.
4.劉淳毅. (2016). 航空燃油價格與上市國籍航空公司股價長期關係之研究 [碩士論文, 國立高雄應用科技大學]. 臺灣博碩士論文知識加值系統. 高雄市.
5.Bashiri Behmiri, N., & Pires Manso, J. R. (2013). Crude oil price forecasting techniques: a comprehensive review of literature. Available at SSRN 2275428.
6.Box George, E., Jenkins Gwilym, M., Reinsel Gregory, C., & Ljung Greta, M. (1976). Time series analysis: forecasting and control. San Francisco: Holden Bay.
7.Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geoscientific model development, 7(3), 1247-1250.
8.Cheng, M.-L., Ching-Wu, C., & Hsiu-Li, H. (2023). A study of univariate forecasting methods for crude oil price. Maritime Business Review, 8(1), 32-47.
9.De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192, 38-48.
10.Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
11.Gao, S., & Lei, Y. (2017). A new approach for crude oil price prediction based on stream learning. Geoscience Frontiers, 8(1), 183-187.
12.Govind, G. R., & Babu, A. S. (2023). Hybrid Deep Learning Model to Forecast Crude Oil Price. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 19-23). IEEE.
13.Graves, A. (2012). Long Short-Term Memory. In A. Graves (Ed.), Supervised Sequence Labelling with Recurrent Neural Networks (pp. 37-45). Springer Berlin Heidelberg.
14.Guo, J. (2019, 8-10 Nov. 2019). Oil Price Forecast Using Deep Learning and ARIMA. 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI),
15.Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780.
16.Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
17.Li, T., Zhong, J., & Huang, Z. (2019). Potential Dependence of Financial Cycles between Emerging and Developed Countries: Based on ARIMA-GARCH Copula Model. Emerging Markets Finance and Trade, 56(6), 1237-1250.
18.Li, Z., Dong, H., Floros, C., Charemis, A., & Failler, P. (2021). Re-examining Bitcoin Volatility: A CAViaR-based Approach. Emerging Markets Finance and Trade, 58(5), 1320-1338.
19.Lim, B., & Zohren, S. (2020). Time Series Forecasting With Deep Learning: A Survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.
20.Mabro, R. (1987). Netback pricing and the oil price collapse of 1986.
21.McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
22.Ng, K. S., Farooq, D., & Yang, A. (2021). Global biorenewable development strategies for sustainable aviation fuel production. Renewable and Sustainable Energy Reviews, 150, 111502.
23.Ouyang, Z.-s., Yang, X.-t., & Lai, Y. (2021). Systemic financial risk early warning of financial market in China using Attention-LSTM model. The North American Journal of Economics and Finance, 56, 101383.
24.Pabuçcu, H., Ongan, S., & Ongan, A. (2020). Forecasting the movements of Bitcoin prices: an application of machine learning algorithms. Quantitative Finance and Economics, 4(4), 679-692.
25.Ronchetti, E. M., & Huber, P. J. (2009). Robust statistics. John Wiley & Sons Hoboken, NJ, USA.
26.Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
27.Rusman, J. A., Chunady, K., Makmud, S. T., Setiawan, K. E., & Hasani, M. F. (2023). Crude Oil Price Forecasting: A Comparative Analysis of ARIMA, GRU, and LSTM Models. In 2023 IEEE 9th International Conference on Computing, Engineering and Design (ICCED) (pp. 1-6). IEEE.
28.Tukey, J. W. (1977). Exploratory data analysis (Vol. 2). Reading, MA.
29.Wallach, D., & Goffinet, B. (1989). Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecological modelling, 44(3-4), 299-306.
30.Willmott, C. J., & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate research, 30(1), 79-82.
31.Wu, N., Green, B., Ben, X., & O′Banion, S. (2020). Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint arXiv:2001.08317.
32.Yamak, P. T., Yujian, L., & Gadosey, P. K. (2019). A comparison between arima, lstm, and gru for time series forecasting. Proceedings of the 2019 2nd international conference on algorithms, computing and artificial intelligence,
33.Zakeri, B., Paulavets, K., Barreto-Gomez, L., Echeverri, L. G., Pachauri, S., Boza-Kiss, B., Zimm, C., Rogelj, J., Creutzig, F., Ürge-Vorsatz, D., Victor, D. G., Bazilian, M. D., Fritz, S., Gielen, D., McCollum, D. L., Srivastava, L., Hunt, J. D., & Pouya, S. (2022). Pandemic, War, and Global Energy Transitions. Energies, 15(17), Article 6114.
34.Zhang, K., & Hong, M. (2022). Forecasting crude oil price using LSTM neural networks. Data Science in Finance and Economics, 2(3), 163-180.

指導教授

胡雅涵(Ya-Han Hu)

審核日期

2024-7-9

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