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A Combined Model Based on GRU with Mahalanobis Distance for Oil Price Prediction

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Web and Big Data (APWeb-WAIM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13422))

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Abstract

Oil plays a key role in economic development. It could help people to make plans and decisions. Because oil price is affected by some factors, it is difficult for people to predict it accurately with current existing models. With the development of deep learning, it is often used to solve multivariate nonlinear problems. In this paper, a combined model based on gated recurrent units is proposed, Mahalanobis distance is used to eliminate outliers and the multivariate nonlinear regression model is constructed, Spearman correlation coefficient is used as feather selection and evaluation metric. Experiment shows that the proposed combined method performs better and could predict the price of crude oil effectively.

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References

  1. Li, X., Shang, W., Wang, S.: Text-based crude oil price forecasting, a deep learning approach. Int. J. Forecast. 35, 1548–1560 (2019)

    Article  Google Scholar 

  2. Chen, Y., He, K., Tso, G.K.F.: Forecasting crude oil prices: a deep learning based on model. Procedia Comput. Sci. 122, 588–595 (2017)

    Article  Google Scholar 

  3. Chen, Y., Zou, Y., Zhou, Y., Zhang, C.: Multi-step-ahead crude oil price forecasting based on grey wave forecasting method. Procedia Comput. Sci. 91, 1050–1056 (2016)

    Article  Google Scholar 

  4. Gumus, M., Kiran, M.S.: Crude oil price forecasting using XGBoost. In: International Conference on Computer Science and Engineering, pp.1100–1103. IEEE, Antalya (2017)

    Google Scholar 

  5. Luo, Z., Cai, X., Tanaka, K., Takiguchi, T., Kinkyo, T., Hamori, S.: Can we forecast daily oil futures prices? Experimental evidence from convolutional neural networks. Risk Finan. Manage. 2(12), 519–530 (2019)

    Google Scholar 

  6. Salvi, H., Shah, A., Mehta, M., Correia, S.: Long Short-term model for Brent oil price forecasting. Eng. Tech. 5(7), 315–319 (2019)

    Google Scholar 

  7. Mahajan, R., Mansotra, V.: Predicting geolocation of tweets: using combination of CNN and BiLSTM. Data Sci. Eng. 6(4), 402–410 (2021)

    Article  Google Scholar 

  8. Sugiartawan, P., Pulungan, R., Kartika Sari, A.: Prediction by a hybrid of wavelet transform and long-short-term-memory neural network. Int. J. Adv. Comput. Sci. Appl. 8(2), 287–296 (2017)

    Google Scholar 

  9. Cen, Z., Wang, J.: Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer. Energy 169, 160–171 (2019)

    Article  Google Scholar 

  10. Urolagin, S., Sharma, N.: Tapan Kumar Datta: A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting. Energy 231, 975–987 (2021)

    Article  Google Scholar 

  11. Wu, J., Li, B., Ji, Ye., Tian, J., Xiang, Y.: Text-enhanced knowledge graph representation model in hyperbolic space. In: Li, B., Yue, L., Jiang, J., Chen, W., Li, X., Long, G., Fang, F., Yu, H. (eds.) ADMA 2022. LNCS (LNAI), vol. 13088, pp. 137–149. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-95408-6_11

    Chapter  Google Scholar 

  12. Titouna, C., Titouna, F.: Outlier detection algorithm based on mahalanobis distance for wireless sensor networks. In: Proceedings of the 2019 International Conference on Computer Communication and Informatics, pp.1567–1576. IEEE, Coimbatore (2019)

    Google Scholar 

  13. Drumond, D.A., Rolo, R.M., Costa, J.F.C.L.: Using Mahalanobis distance to detect and remove outliers in experimental covariograms. Nat. Resour. Res. 28, 1056–1067 (2018)

    Google Scholar 

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Shichen Zhai & Zongmin Ma

Authors
  1. Shichen Zhai
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  2. Zongmin Ma
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Corresponding author

Correspondence to Zongmin Ma .

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. Newcastle University, Callaghan, NSW, Australia

    Lin Yue

  3. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Chuanqi Tao

  4. Jinan University, Guangzhou, China

    Xuming Han

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Diego Calvanese

  6. University of Tsukuba, Tsukuba, Japan

    Toshiyuki Amagasa

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhai, S., Ma, Z. (2023). A Combined Model Based on GRU with Mahalanobis Distance for Oil Price Prediction. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_43

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  • DOI: https://doi.org/10.1007/978-3-031-25198-6_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25197-9

  • Online ISBN: 978-3-031-25198-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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