||Indexmundi. https://www.indexmundi.com. Accessed: 2019-06-25. |
Investing.com. https://www.investing.com. Accessed: 2019-06-25.
 Taipower website. https://www.taipower.com.tw/tc/page.aspx?mid=197. Accessed: 2019-06-24.
 Yahoo finance. https://finance.yahoo.com. Accessed: 2019-06-24.
 Ryo Akita, Akira Yoshihara, Takashi Matsubara, and Kuniaki Uehara. Deep learning for stock prediction using numerical and textual information. In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pages 1–6. IEEE, 2016.
 Sean Bell and Kavita Bala. Learning visual similarity for product design with con- volutional neural networks. ACM Transactions on Graphics (TOG), 34(4):98, 2015.
 Anastasia Borovykh, Sander Bohte, and Cornelis W Oosterlee. Conditional time se- ries forecasting with convolutional neural networks. arXiv preprint arXiv:1703.04691, 2017.
 Jou-Fan Chen, Wei-Lun Chen, Chun-Ping Huang, Szu-Hao Huang, and An-Pin Chen. Financial time-series data analysis using deep convolutional neural networks. In 2016 7th International Conference on Cloud Computing and Big Data (CCBD), pages 87– 92. IEEE, 2016.
 Kai Chen, Yi Zhou, and Fangyan Dai. A lstm-based method for stock returns pre- diction: A case study of china stock market. In 2015 IEEE International Conference on Big Data (Big Data), pages 2823–2824. IEEE, 2015.
 Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014.
 Luca Di Persio and Oleksandr Honchar. Recurrent neural networks approach to the financial forecast of google assets. International journal of Mathematics and Computers in simulation, 11, 2017.
 Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. Deep learning for event-driven stock prediction. In Twenty-Fourth International Joint Conference on Artificial In- telligence, 2015.
 Qiyuan Gao. Stock market forecasting using recurrent neural network. PhD thesis, University of Missouri–Columbia, 2016.
 Isaac Madan, Shaurya Saluja, and Aojia Zhao. Automated bitcoin trading via machine learning algorithms. URL: http://cs229. stanford. edu/proj2014/Isaac% 20Madan, 20, 2015.
 Martina Matta, Ilaria Lunesu, and Michele Marchesi. Bitcoin spread prediction using social and web search media. In UMAP Workshops, pages 1–10, 2015.
 Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. Convolutional neural networks over tree structures for programming language processing. In Thirtieth AAAI Con- ference on Artificial Intelligence, 2016.
 Jigar Patel, Sahil Shah, Priyank Thakkar, and K Kotecha. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1):259–268, 2015.
 Akhter Mohiuddin Rather, Arun Agarwal, and VN Sastry. Recurrent neural network and a hybrid model for prediction of stock returns. Expert Systems with Applications, 42(6):3234–3241, 2015.
 Abhishek Sehgal and Nasser Kehtarnavaz. A convolutional neural network smart- phone app for real-time voice activity detection. IEEE Access, 6:9017–9026, 2018.
 Devavrat Shah and Kang Zhang. Bayesian regression and bitcoin. In 2014 52nd annual Allerton conference on communication, control, and computing (Allerton), pages 409–414. IEEE, 2014.
 Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis. Forecasting stock prices from the limit order book using convolutional neural networks. In 2017 IEEE 19th Conference on Business Informatics (CBI), volume 1, pages 7–12. IEEE, 2017.
 Jian Yang, Ruonan Rao, Pei Hong, and Peng Ding. Ensemble model for stock price movement trend prediction on different investing periods. In 2016 12th International Conference on Computational Intelligence and Security (CIS), pages 358–361. IEEE, 2016.
 Liheng Zhang, Charu Aggarwal, and Guo-Jun Qi. Stock price prediction via discov- ering multi-frequency trading patterns. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 2141–2149. ACM, 2017.