A hybrid machine learning method for procurement risk assessment of non-ferrous metals for manufacturing firms

References

• Althelaya, K. A., E.-S. El-Alfy, and S. Mohammed (2018). Evaluation of Bidirectional LSTM for Short-and Long-term Stock Market Prediction. 9th International Conference on Information and Communication Systems (ICICS),  Irbid, Jordan.
   Google Scholar
• Azadeh, A., M. Moghaddam, M. Khakzad, and V. Ebrahimipour. 2012. “A Flexible Neural Network-fuzzy Mathematical Programming Algorithm for Improvement of Oil Price Estimation and Forecasting.” Computers & Industrial Engineering 62 (2): 421–430. doi:10.1016/j.cie.2011.06.019.
   Web of Science ®Google Scholar
• Bildirici, M., and Ö. Ö. Ersin. 2009. “Improving Forecasts of GARCH Family Models with the Artificial Neural Networks: An Application to the Daily Returns in Istanbul Stock Exchange.” Expert Systems with Applications 36 (4): 7355–7362. doi:10.1016/j.eswa.2008.09.051.
   Web of Science ®Google Scholar
• Bildirici, M., and Ö. Ö. Ersin. 2013. “Forecasting Oil Prices: Smooth Transition and Neural Network Augmented GARCH Family Models.” Journal of Petroleum Science and Engineering 109: 230–240. doi:10.1016/j.petrol.2013.08.003.
   Web of Science ®Google Scholar
• Bollerslev, T. 1986. “Generalized Autoregressive Conditional Heteroskedasticity.” Journal of Econometrics 31 (3): 307–327. doi:10.1016/0304-4076(86)90063-1.
   Web of Science ®Google Scholar
• Chkili, W., S. Hammoudeh, and D. K. Nguyen. 2014. “Volatility Forecasting and Risk Management for Commodity Markets in the Presence of Asymmetry and Long-memory.” Energy Economics 41: 1–18. doi:10.1016/j.eneco.2013.10.011.
   Web of Science ®Google Scholar
• Eapen, J., D. Bein, and A. Verma (2019). Novel Deep Learning Model with CNN and Bi-directional LSTM for Improved Stock Market Index Prediction. 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA. 0264–0270.
   Google Scholar
• Engle, R.F. 1982. “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation.” Econometrica 50(4): 987–1007. doi:10.2307/1912773.
   Web of Science ®Google Scholar
• Fischer, T., and C. Krauss. 2018. “Deep Learning with Long Short-term Memory Networks for Financial Market Predictions.” European Journal of Operational Research 270: 654–669. doi:10.1016/j.ejor.2017.11.054.
   Web of Science ®Google Scholar
• Gers, F. A., J. Schmidhuber, and F. Cummins. 2000. “Learning to Forget: Continual Prediction with LSTM.” Neural Computation 12 (10): 2451–2471. doi:10.1162/089976600300015015.
   PubMed Web of Science ®Google Scholar
• Graves, A., A.-R. Mohamed, and G. Hinton (2013). Speech Recognition with Deep Recurrent Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada. 6645–6649.
   Google Scholar
• Guresen, E., G. Kayakutlu, and T. U. Daim. 2011. “Using Artificial Neural Network Models in Stock Market Index Prediction.” Expert Systems with Applications 38 (8): 10389–10397. doi:10.1016/j.eswa.2011.02.068.
   Web of Science ®Google Scholar
• Hamid, S. A., and Z. Iqbal. 2004. “Using Neural Networks for Forecasting Volatility of S&P 500 Index Futures Prices.” Journal of Business Research 57 (10): 1116–1125. doi:10.1016/S0148-2963(03)00043-2.
   Web of Science ®Google Scholar
• Hammoudeh, S., and Y. Yuan. 2008. “Metal Volatility in Presence of Oil and Interest Rate Shocks.” Energy Economics 30 (2): 606–620. doi:10.1016/j.eneco.2007.09.004.
   Web of Science ®Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-term Memory.” Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Khalifa, A. A., H. Miao, and S. Ramchander. 2011. “Return Distributions and Volatility Forecasting in Metal Futures Markets: Evidence from Gold, Silver, and Copper.” Journal of Futures Markets 31 (1): 55–80. doi:10.1002/fut.20459.
   Web of Science ®Google Scholar
• Kim, H. Y., and C. H. Won. 2018. “Forecasting the Volatility of Stock Price Index: A Hybrid Model Integrating LSTM with Multiple GARCH-type Models.” Expert Systems with Applications 103: 25–37. doi:10.1016/j.eswa.2018.03.002.
   Web of Science ®Google Scholar
• Kristjanpoller, R., . W., and P., . E. Hernández. 2017. “Volatility of Main Metals Forecasted by a Hybrid ANN-GARCH Model with Regressors.” Expert Systems with Applications 84: 290–300. doi:10.1016/j.eswa.2017.05.024.
   Web of Science ®Google Scholar
• Kristjanpoller, W., A. Fadic, and M. C. Minutolo. 2014. “Volatility Forecast Using Hybrid Neural Network Models.” Expert Systems with Applications 41 (5): 2437–2442. doi:10.1016/j.eswa.2013.09.043.
   Web of Science ®Google Scholar
• Kristjanpoller, W., and M. Minutolo. 2015. “Gold Price Volatility: A Forecasting Approach Using the Artificial Neural network–GARCH Model.” Expert System with Applications 42 (5): 7245–7251. doi:10.1016/j.eswa.2015.04.058.
   Google Scholar
• Kristjanpoller, W., and M. Minutolo. 2016. “Forecasting Volatility of Oil Price Using an Artificial Neural network-GARCH Model.” Expert System with Applications 65 (15): 233–241. doi:10.1016/j.eswa.2016.08.045.
   Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521: 436–444. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Liang, P., H. D. Yang, W. S. Chen, S.-Y. Xiao, -Z.-Z. Lan et al. 2017. “Transfer Learning for Aluminium Extrusion Electricity Consumption Anomaly Detection via Deep Neural Networks.” International Journal of Computer Integrated Manufacturing 31 (4–5): 396–405. doi:10.1080/0951192X.2017.1363410.
   Web of Science ®Google Scholar
• Liu, Y. S., C. H. Yang, K. K. Huang, and W. H. Gui. 2019. “Non-ferrous Metals Price Forecasting Based on Variational Mode Decomposition and LSTM Network.” Knowledge-Based Systems, 188(5): 105006. doi:10.1016/j.knosys.2019.105006.
   Google Scholar
• Maknickienė, N., and A. Maknickas (2012). “Application of Neural Network for Forecasting of Exchange Rates and Forex Trading.” Proceedings of the 7th international scientific conference on business and management, Vilnius, Lithuania. 10–11.
   Google Scholar
• Nelson, D. M. Q., A. C. M. Pereira, and R. A. D. Oliveira. (2017). “Stock Market’s Price Movement Prediction with LSTM Neural Networks.” 2017 International Joint Conference on Neural Networks, Anchorage, AK, USA. 1419–1426.
   Google Scholar
• Ou, T. Y., C. Y. Cheng, P. J. Chen, C. Perng, and J. T. Tsai. 2016. “Dynamic Cost Forecasting Model Based on Extreme Learning Machine - A Case Study in Steel Plant.” Computers & Industrial Engineering 101: 544–553. doi:10.1016/j.cie.2016.09.012.
   Web of Science ®Google Scholar
• Parisi, A., F. Parisi, and D. Díaz. 2008. “Forecasting Gold Price Changes: Rolling and Recursive Neural Network Models.” Journal of Multinational Financial Management 18 (5): 477–487. doi:10.1016/j.mulfin.2007.12.002.
   Google Scholar
• Trück, S., and K. Liang. 2012. “Modelling and Forecasting Volatility in the Gold Market.” International Journal of Banking and Finance 9: 1.
   Google Scholar
• Tsay, R. S. 2010. Analysis of Financial Time Series. 3rd Edition ed. Hoboken, New Jersey, USA: John Wiley & Sons, Inc.
   Google Scholar
• Wang, H. B., Z. P. Yang, Q. Yu, T. J. Hong, and X. Lin. 2018. “Online Reliability Time Series Prediction via Convolutional Neural Network and Long Short Term Memory for Service-oriented Systems.” Knowledge-Based Systems 159: 132–147. doi:10.1016/j.knosys.2018.07.006.
   Web of Science ®Google Scholar
• Wang, J., M. M. Hu, and J. F. D. Rodrigues. 2018. “The Evolution and Driving Forces of Industrial Aggregate Energy Intensity in China: An Extended Decomposition Analysis.” Applied Energy 228: 2195–2206. doi:10.1016/j.apenergy.2018.07.039.
   Web of Science ®Google Scholar
• Wöllmer, M., M. Kaiser, F. Eyben, B. Schuller, and G. Rigoll. 2013. “LSTM-Modeling of Continuous Emotions in an Audiovisual Affect Recognition Framework.” Image and Vision Computing 31: 153–163. doi:10.1016/j.imavis.2012.03.001.
   Web of Science ®Google Scholar
• Yazdani-Chamzini, A., S. H. Yakhchali, D. Volungevičienė, and E. K. Zavadskas. 2012. “Forecasting Gold Price Changes by Using Adaptive Network Fuzzy Inference System.” Journal of Business Economics and Management 13 (5): 994–1010. doi:10.3846/16111699.2012.683808.
   Web of Science ®Google Scholar
• Yeh, T. H., and S. Deng. 2012. “Application of Machine Learning Methods to Cost Estimation of Product Life Cycle.” International Journal of Computer Integrated Manufacturing 25 (4–5): 340–352. doi:10.1080/0951192X.2011.645381.
   Web of Science ®Google Scholar
• Zhang, J., Z. Tan, and S. Yang. 2012. “Day-ahead Electricity Price Forecasting by a New Hybrid Method.” Computers & Industrial Engineering 63: 695–701. doi:10.1016/j.cie.2012.03.016.
   Web of Science ®Google Scholar
• Zhong, M. R., R. F. He, J. Y. Chen, and J. B. Huang. 2019. “Time-varying Effects of International Nonferrous Metal Price Shocks on China’s Industrial Economy. Physica A.” Statistical Mechanics and Its Application 528: 121–299. doi:10.1016/j.physa.2019.121299.
   Web of Science ®Google Scholar