대한건축학회논문집 (Journal of the Architectural Institute of Korea)

  • 제36권12호
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  • Pages.265-272
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  • 2020
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  • 2733-6239(pISSN)
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  • 2733-6247(eISSN)
대한건축학회 (Architectural Institute of Korea)
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딥러닝을 활용한 철근 가격 단기예측범위 확대에 관한 실험적 연구

Experimental Study on the Expansion of the Short-term Prediction Range of Rebar Prices Using Deep Learning

  • 투고 : 2020.10.14
  • 심사 : 2020.12.12
  • 발행 : 2020.12.30
⟨ 이전 논문 다음 논문 ⟩

초록

This study presents a method for expanding the prediction range of rebar price prediction using the short-term prediction method of deep learning. In general, the prediction range of a short-term prediction is dependent on the time interval of the data to be entered, so it can be expanded by adjusting the time interval of the data. However, as the range of forecasts increases, the size of the data decreases, which can lead to overfitting that cannot guarantee good results. The average accuracy of the forecasts is approximately 98.49% when the scope of the forecasts is extended from 1 month to 2 and 3 months with the proposed approach presented in this study. In addition, this approach could be used as a basis for expanding the predictive range of deep learning in a study that predicts prices with time series data including common building materials.

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참고문헌

  1. Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization, The Journal of Machine Learning Research, 13(1), pp. 281-305.
  2. Choi, Y., Yim, H., & Park, B. (2009). Analysis on the Lotting Price Fluctuation of the Multi-Family Attached House According to the Construction Material Cost Variation, Journal of The Korean Society of Civil Engineers, 29(6D), pp. 753-760.
  3. Choi, M., & Kwon, O. (2008). Construction material cost increase and countermeasures, Construction trend briefing by Korea Institute of Construction Industry, Vol. 2008 No.6, pp. 2-34.
  4. Chung, W., Park, G., Gu, Y., Kim, S., & Yoo, S. (2019). City Gas Pipeline Pressure Prediction Model, Journal of Society for e-Business Studies, 23(2), pp. 33-47. https://doi.org/10.7838/JSEBS.2018.23.2.033
  5. Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions, European Journal of Operational Research, 270(2), pp. 654-669. https://doi.org/10.1016/j.ejor.2017.11.054
  6. Gao, X., Shi, M., Song, X., Zhang, C., & Zhang, H. (2019). Recurrent neural networks for real-time prediction of TBM operating parameters, Automation in Construction, 98, pp. 225-235. https://doi.org/10.1016/j.autcon.2018.11.013
  7. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory, Neural computation, 9(8), pp. 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735
  8. Joo, I., & Choi, S. (2018). Stock prediction model based on bidirectional LSTM recurrent neural network, The Journal of Korea Institute of Information, Electronics, and Communication Technology, 11(2), pp. 204-208. https://doi.org/10.17661/JKIIECT.2018.11.2.204
  9. Kim, J., & Baek, C. (2019) Bivariate long range dependent time series forecasting using deep learning, The Korean Journal of Applied Statistics, 32(1), pp. 69-81. https://doi.org/10.5351/KJAS.2019.32.1.069
  10. Lahari, M. C., Ravi, D. H., & Bharathi, R. (2018). Fuel Price Prediction Using RNN, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1510-1514. IEEE.
  11. Larochelle, H., Erhan, D., Courville, A., Bergstra, J., & Bengio, Y. (2007). An empirical evaluation of deep architectures on problems with many factors of variation, In Proceedings of the 24th international conference on Machine learning, pp. 473-480.
  12. Lee, D., & Kim, K. (2019). Deep Learning Based Prediction Method of Long-term Photovoltaic Power Generation Using Meteorological and Seasonal Information, Journal of Society for e-Business Studies, 24(1), pp. 1-16.
  13. Lee, J., & Choi, J. (2007). Analysis of the impact of material procurement and procurement management on the process: Focusing on the steel structure project, Journal of the Architectural Institute of Korea-Structural Section, 23.12, pp. 141-148.
  14. Lee, J., Yoo, J., Kim., C., Lee, G., & Lim, B. (2008). The method of calculating the order point considering the fluctuations in demand for materials at construction sites. Journal of the Architectural Institute of Korea-Structural System, 24(10), pp. 117-125.
  15. Lee, S., Kim, S., Lee, J., & Han, C. (2006). Proposal for Developed Procurement and Material management System On Using Previous System Analysis in Plant Engineering, Korean Society for Construction Management Conference, pp. 204-209.
  16. Lee, W., Kim, Y., Kim, J., & Lee, C. (2020). Forecasting of Iron Ore Prices using Machine Learning, Journal of the Korea Industrial Information Systems Research, 25(2), pp. 57-72. https://doi.org/10.9723/JKSIIS.2020.25.2.057
  17. Liao, T. W. (2005). Clustering of time series data-a survey, Pattern recognition, 38(11), pp. 1857-1874. https://doi.org/10.1016/j.patcog.2005.01.025
  18. Liu, H., Mi, X., & Li, Y. (2018). Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network, Energy Conversion and Management, 166, pp. 120-131. https://doi.org/10.1016/j.enconman.2018.04.021
  19. Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F. Y. (2014). Traffic flow prediction with big data: a deep learning approach, IEEE Transactions on Intelligent Transportation Systems, 16(2), pp. 865-873. https://doi.org/10.1109/TITS.2014.2345663
  20. Olson, D. L., & Delen, D. (2008). Advanced data mining techniques, Springer-Verlag, Berlin Heidelberg.
  21. Park, S., & Jung, D.(2016). Basic Study on the Material Market by Specialized Construction Industry, Seoul: Korea Institute of Construction Policy and Management.
  22. Polson, N.G., & Sokolov, V.O. (2017). Deep learning for short-term traffic flow prediction, Transportation Research Part C: Emerging Technologies, 79, pp. 1-17. https://doi.org/10.1016/j.trc.2017.02.024
  23. Schuster, M., & Paliwal, K.K. (1997). Bidirectional recurrent neural networks, IEEE Trans Signal Process, 45, pp. 2673-2681. https://doi.org/10.1109/78.650093
  24. Seo, Y, & Yeom, J. (2019). Comparison of LSTM-based Fine Dust Concentration Prediction Method using Meteorology Data, Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography 2019, pp. 117-120.
  25. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting, The journal of machine learning research, 15(1), pp. 1929-1958.
  26. Tensorflow.org. (2020) Overfitting and underfitting, accessed Sep 27, 2020, https://www.tensorflow.org/tutorials/keras/overfit_and_underfit.stand