- Y. Zhang, X. Tan, H. Xi, and X. Zhao, “Real-time risk management based on time series analysis,” in 2008 7th World Congress on IntelligentControl and Automation. IEEE, 2008, pp. 2518–2523.Google ScholarCross Ref
- D. Giamouridis and I. D. Vrontos, “Hedge fund portfolio construction: A comparison of static and dynamic approaches,” Journal of Banking & Finance, vol. 31, no. 1, pp. 199–217, 2007.Google ScholarCross Ref
- X. Zhou, Z. Pan, G. Hu, S. Tang, and C. Zhao, “Stock market prediction on high-frequency data using generative adversarial nets,” Mathematical Problems in Engineering, vol. 2018, pp. 1–11, 2018.Google Scholar
- A. Harvey and P. Todd, “Forecasting economic time series with structural and box-jenkins models: A case study,” Journal of Business Economic Statistics, vol. 1, pp. 299–307, 1983.Google Scholar
- X. Tian, J. Zhang, Z. Ma, Y. He, J. Wei, P. Wu, W. Situ, S. Li, and Y. Zhang, “Deep lstm for large vocabulary continuous speech recognition,” arXiv preprint arXiv:1703.07090, 2017.Google Scholar
- R. K. Agrawal, F. Muchahary, and M. M. Tripathi, “Long term load forecasting with hourly predictions based on long-short-term-memory networks,” in 2018 IEEE Texas Power and Energy Conference (TPEC). IEEE, 2018, pp. 1–6.Google ScholarCross Ref
- I. Khandelwal, U. Satija, and R. Adhikari, “Forecasting seasonal time series with functional link artificial neural network,” in 2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2015, pp. 725–729.Google ScholarCross Ref
- J. D. Power, M. Plitt, S. J. Gotts, P. Kundu, V. Voon, P. A. Bandettini,and A. Martin, “Ridding fmri data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data,” Proceedings of the National Academy of Sciences, vol. 115, no. 9, pp. E2105–E2114, 2018.Google ScholarCross Ref
- C. Gargour, M. Gabrea, V. Ramachandran, and J.-M. Lina, “A short introduction to wavelets and their applications,” IEEE circuits and systems magazine, vol. 9, no. 2, pp. 57–68, 2009. Google ScholarDigital Library
- Y. Qin, D. Song, H. Chen, W. Cheng, G. Jiang, and G. Cottrell, “A dual-stage attention-based recurrent neural network for time series prediction,” arXiv preprint arXiv:1704.02971, 2017. Google ScholarDigital Library
- S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997. Google ScholarDigital Library
- L. Jing-yi, L. Hong, Y. Dong, and Z. Yan-sheng, “A new wavelet threshold function and denoising application,” Mathematical Problems in Engineering, vol. 2016, 2016.Google Scholar
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature, vol. 323, no. 6088, pp. 533–536, 1986.Google Scholar
- N. Kalchbrenner and P. Blunsom, “Recurrent continuous translation models,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 2013, pp. 1700–1709.Google Scholar
- K. Cho, B. Van Merri¨enboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,”arXiv preprint arXiv:1409.1259, 2014.Google Scholar
- K. Cho, B. Van Merri¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.Google ScholarCross Ref
- I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in neural information processing systems, vol. 27, pp. 3104–3112, 2014. Google ScholarDigital Library
- K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio, “Show, attend and tell: Neural image caption generation with visual attention,” in International conference on machine learning, 2015, pp. 2048–2057. Google ScholarDigital Library
- T. Parcollet, M. Ravanelli, M. Morchid, G. Linar`es, C. Trabelsi, R. De Mori, and Y. Bengio, “Quaternion recurrent neural networks,”arXiv preprint arXiv:1806.04418, 2018.Google Scholar
- J. Qiu, B. Wang, and C. Zhou, “Forecasting stock prices with long-short term memory neural network based on attention mechanism,” PloS one, vol. 15, no. 1, p. e0227222, 2020Google ScholarCross Ref
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Perfecting Short-term Stock Predictions with Multi-Attention Networks in Noise-free Settings
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