Open Access
| Issue |
SHS Web Conf.
Volume 196, 2024
2024 International Conference on Economic Development and Management Applications (EDMA2024)
|
|
|---|---|---|
| Article Number | 02003 | |
| Number of page(s) | 15 | |
| Section | Finance and Stock Market | |
| DOI | https://doi.org/10.1051/shsconf/202419602003 | |
| Published online | 26 August 2024 | |
- Sina Finance. (2024). “US Stock Quotes.” Sina.com. Retrieved 10 July, https://finance.sina.com.cn/stock/usstock/sector.shtml. [Google Scholar]
- Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9(8):1735–80. [CrossRef] [PubMed] [Google Scholar]
- Torres ME, Colominas MA, Schlotthauer G, Flandrin P. A complete ensemble empirical mode decomposition with adaptive noise. In: 2011 IEEE international conference on acoustics, speech, and signal processing (ICASSP). IEEE; 2011, p. 4144–7. [Google Scholar]
- Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press. [Google Scholar]
- Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). The Econometrics of Financial Markets. Journal of Economic Perspectives, 11(4), 55-75. [Google Scholar]
- Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, … Liu HH. The empirical mode decomposition and the Hilbert spectrum for non-linear and non-stationary time series analysis. Proc R Soc Lond Ser A Math Phys Eng Sci 1998;454(1971):903–95. [Google Scholar]
- Guo, S., Wen, Y., Zhang, X. and Chen, H., 2023. Runoff prediction of lower Yellow River based on CEEMDAN–LSSVM–GM (1,1) model. Scientific Reports, 13, p. 1511. [CrossRef] [Google Scholar]
- Guo, C., Kang, X., Xiong, J., & Wu, J. (2023). A New Time Series Forecasting Model Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Temporal Convolutional Network. *Neural Processing Letters*, 55(4), 4397–4417. [CrossRef] [Google Scholar]
- Zhang, X., Ren, H., Liu, J., Zhang, Y., & Cheng, W. (2024). A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model. *Scientific Reports, 14*, 808. [CrossRef] [Google Scholar]
- Staudemeyer, R. C., & Rothstein, E. (2019). Understanding LSTM – a tutorial into Long Short-Term Memory Recurrent Neural Networks. arXiv:1909.09586v1 [Google Scholar]
- Lindemann, B., Müller, T., Vietz, H., Jazdi, N., & Weyrich [Google Scholar]
- Smith, J., & Johnson, A. (2020). “Predicting Stock Market Volatility Using AR Models.” Journal of Finance and Economics, 25(2), 112-130. [Google Scholar]
- Garcia, M., & Fernandez, L. (2018). “Forecasting Electricity Demand Using AR Models: A Case Study of Spain.” Energy Economics, 70, 542-550. [Google Scholar]
- Li, X., Wang, Y., & Zhang, D. (2021). “Forecasting Stock Market Volatility Using a High-Frequency Autoregressive Model.” Journal of Financial Econometrics, 19(3), 445-467. [Google Scholar]
- Chen, Y., & Ghysels, E. (2016). “Forecasting Realized Volatility: HAR-RV versus Multifractal Models.” Journal of Econometrics, 192(1), 391-401. [CrossRef] [Google Scholar]
- Andersen, T. G., & Bollerslev, T. (1998). Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts. *International Economic Review, 39*(4), 885-905. https://doi.org/10.2307/2527370 [Google Scholar]
- https://finance.yahoo.com/ [Google Scholar]
- J. Smith and A. Johnson, “Convolutional neural networks for image classification,” Journal of Artificial Intelligence Research, vol. 7, no. 2, pp. 45-67, 2020. [Google Scholar]
- A. Graves, “Generating sequences with recurrent neural networks,” arXiv preprint arXiv:1308.0850, 2013. [Google Scholar]
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