Stock markets of today, and will continue to in the future, rely on the metrics of timeliness and efficiency to reach optimal profits. A way stock investors have continued to strive for the best of these two factors of the business is through the use of predictive machine learning systems to help aid in their decision making. However, among the many systems currently in use, it could be said that the myriad of data that they are based on may not be sufficient. In an effort to devise an ensemble learning predictive system that will utilize an array of big data sources, we conducted research into the use of long-term short-term recurrent neural networks in stock prediction and planned experiments around the timeliness for it to be an effective implementation into our proposed predictive system.
Landis, W. M., & Cha, S. (2020). Towards High Performance Stock Market Prediction Methods. IEEE Cloud Summit 2020, 1-5. Retrieved from https://digitalcommons.harrisburgu.edu/cisc_student-coursework/3
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