| Reference: | Atil, H., & Unver, Y. (2000). A different approach of experimental design: Taguchi method. Pakistan journal of biological sciences, 3(9), 1538-1540.
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., & Choo, J. (2018). Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
Choi, Y., Uh, Y., Yoo, J., & Ha, J.-W. (2020). Stargan v2: Diverse image synthesis for multiple domains. Paper presented at the Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.
Dey, R., & Salem, F. M. (2017). Gate-variants of gated recurrent unit (GRU) neural networks. Paper presented at the 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS).
Fu, R., Zhang, Z., & Li, L. (2016). Using LSTM and GRU neural network methods for traffic flow prediction. Paper presented at the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
Homayouni, N., & Amiri, A. (2011). Stock price prediction using a fusion model of wavelet, fuzzy logic and ANN. Paper presented at the International conference on e-business, management and economics.
Huang, Y.-P., Chen, S.-H., Hung, M.-C., & Yu, T. (2012). Liquidity cost of market orders in the Taiwan Stock Market: A study based on an order-driven agent-based artificial stock market. International Review of Financial Analysis, 23, 72-80.
Karna, S. K., & Sahai, R. (2012). An overview on Taguchi method. International journal of engineering and mathematical sciences, 1(1), 1-7.
Kumar, D., Sarangi, P. K., & Verma, R. (2021). A systematic review of stock market prediction using machine learning and statistical techniques. Materials Today: Proceedings.
Lahmiri, S. (2014). Wavelet low-and high-frequency components as features for predicting stock prices with backpropagation neural networks. Journal of King Saud University-Computer and Information Sciences, 26(2), 218-227.
Lan, P.-C., Kung, W.-L., Ou, Y.-L., Lin, C.-Y., Hu, W.-C., & Wang, Y.-H. (2019). Machine learning model with technical analysis for stock price prediction: Empirical study of Semiconductor Company in Taiwan. Paper presented at the 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).
Li, C.-L., Chang, W.-C., Cheng, Y., Yang, Y., & Póczos, B. (2017). Mmd gan: Towards deeper understanding of moment matching network. Advances in neural information processing systems, 30.
Lim, J. H., & Ye, J. C. (2017). Geometric gan. arXiv preprint arXiv:1705.02894.
Lin, F.-L., Yang, S.-Y., Marsh, T., & Chen, Y.-F. (2018). Stock and bond return relations and stock market uncertainty: Evidence from wavelet analysis. International Review of Economics & Finance, 55, 285-294.
Lucic, M., Kurach, K., Michalski, M., Gelly, S., & Bousquet, O. (2018). Are gans created equal? a large-scale study. Advances in neural information processing systems, 31.
Porav, H., Musat, V., & Newman, P. (2019). Reducing Steganography In Cycle-consistency GANs. Paper presented at the CVPR Workshops.
Reboredo, J. C., & Rivera-Castro, M. A. (2014). Wavelet-based evidence of the impact of oil prices on stock returns. International Review of Economics & Finance, 29, 145-176.
Rhif, M., Ben Abbes, A., Farah, I. R., Martínez, B., & Sang, Y. (2019). Wavelet transform application for/in non-stationary time-series analysis: a review. Applied Sciences, 9(7), 1345.
Saiti, B. (2017). The Lead-Lag Relationship among East Asian Economies: A Wavelet Analysis. International Business Research, 10(3), 57-68.
Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
Taguchi, G. (1995). Quality engineering (Taguchi methods) for the development of electronic circuit technology. IEEE Transactions on Reliability, 44(2), 225-229.
Thiele, J. C., Bichler, O., & Dupret, A. (2019). Spikegrad: An ann-equivalent computation model for implementing backpropagation with spikes. arXiv preprint arXiv:1906.00851.
Virgilio, G. P. M. (2019). High-frequency trading: a literature review. Financial markets and portfolio management, 33(2), 183-208.
Wang, W., Sun, Y., & Halgamuge, S. (2018). Improving MMD-GAN training with repulsive loss function. arXiv preprint arXiv:1812.09916.
Yang, S., Yu, X., & Zhou, Y. (2020). Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example. Paper presented at the 2020 International workshop on electronic communication and artificial intelligence (IWECAI).
Yaz, Y., Foo, C.-S., Winkler, S., Yap, K.-H., Piliouras, G., & Chandrasekhar, V. (2018). The unusual effectiveness of averaging in GAN training. Paper presented at the International Conference on Learning Representations.
Zhang, C., Tang, Y., Zhao, C., Sun, Q., Ye, Z., & Kurths, J. (2021). Multitask gans for semantic segmentation and depth completion with cycle consistency. IEEE Transactions on Neural Networks and Learning Systems, 32(12), 5404-5415.
Zhang, K., Zhong, G., Dong, J., Wang, S., & Wang, Y. (2019). Stock market prediction based on generative adversarial network. Procedia computer science, 147, 400-406.
Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). Stock market prediction on high-frequency data using generative adversarial nets. Mathematical Problems in Engineering.
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. Paper presented at the Proceedings of the IEEE international conference on computer vision.
Zieba, M., & Wang, L. (2017). Training triplet networks with GAN. arXiv preprint arXiv:1704.02227.
吳宗哲. (2019). 運用深度學習建構個股預測模型-以台積電為例.
李中永. (2014). 使用類神經網路結合模糊理論預測標準普爾500指數. (碩士). 國立高雄海洋科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/3y5849
杜芸菩. (2016). 台灣八大類股價量關係. (碩士). 國立政治大學, 台北市. Retrieved from https://hdl.handle.net/11296/sn9fng
林奕廷. (2019). 以循環生成對抗網路預測股價量能動態關係.
林逸婷. (2011). 倒傳遞類神經網路於股價交易點之預測. (碩士). 國立高雄第一科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/d2nn27
莊向峰. (2018). 基於行為經濟學與價量分析使用增強式學習演算法建立臺灣股票指數期貨交易策略.
陳俊諺. (2018). 運用類神經網路與田口法預測台灣 ETF 指數.
陳家騏. (2016). 牛頓第二運動定律的演變--從速度, 力到動量, 衝量, 以單一質點到多質點 (系統). 科學教育月刊(393), 11-19.
曾琬婷. (2017). 台灣加權股價指數之預測模型: 小波轉換與多項式迴歸模型之應用.
游英裕. (2004). 股價與成交量因果關係之研究-台灣股市的實証. 碩士,
葉雅玲. (2010). 產業別股票價量關係.
潘永浤. (2003). 應用田口方法於類神經網路輸入參數設計-零售商快速回應系統模式之建立為例. 碩士,
蔡尚翰. (2017). 籌碼面選股結合技術分析之投資績效研究.
賴建成. (2012). 小波轉換結合類神經網路於股價預測及價格發現之研究－以香港及中國指數現貨與期貨對台港兩地掛牌ETF為例. (博士). 國立高雄第一科技大學, 高雄市. Retrieved from https://hdl.handle.net/11296/rbbbeq
謝璁賦, & 陳安斌. (2010). 應用類神經網路於台股權值股籌碼面的知識發現.
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