by Qingyun Wang and Yayuan Xiao
Original Research
In recent decades, Long Short-Term Memory networks (LSTMs), an enhanced version of Recurrent Neural Networks (RNNs), have made significant contributions across various domains. Particularly in the study of time series data, they have offered promising capabilities in capturing temporal dependencies and patterns. This paper delves into the application of LSTMs in market forecasting, aiming to use historical price data to construct predictive models and optimize investment allocations for improved portfolio performance. The investigation includes a detailed examination of hyperparameters tailored for Invesco QQQ Trust (QQQ), SPDR Gold Trust (GLD), and Bitcoin (BTC) LSTM models, employing them for price prediction and the development of high-return trading strategies. Following this, an analysis is carried out on portfolio holdings, return rates, and risk enhancements for each investment asset within the testing set under this trading strategy.
American Journal of Applied Mathematics and Statistics. 2024, 12(1), 15-23. DOI: 10.12691/ajams-12-1-3
Pub. Date: February 22, 2024
by Michael Gr. Voskoglou
Original Research
The APOS/ACE instructional treatment for teaching mathematics was introduced in the USA by Prof. Ed Dubinsky and his research team during the 1990’s The central idea of the APOS/ACE treatment is that one can always find a suitable computer task for helping students to build the mental constructions that lead to the learning of the corresponding mathematical topic. In this work a method is presented for assessing the overall performance of a student group when the instructor is not sure about the accuracy of the individual grades assigned to the students. This method is developed using neutrosophic sets as tools and writing their elements in the form of neutrosophic triplets and it is used here for evaluating the effectiveness of the APOS/ACE instructional treatment for teaching mathematics. The outcomes of the classroom application performed for this purpose provide a strong indication that the APOS/ACE approach benefits the mediocre and the weak in mathematics students more than the good students, but this requires further experimental research.
American Journal of Applied Mathematics and Statistics. 2024, 12(1), 10-14. DOI: 10.12691/ajams-12-1-2
Pub. Date: February 01, 2024
by Siqi Mao, Yaping Yuan, Yinpu Li, Ziren Wang, Yuanxin Yao and Yixin Kang
Original Research
Energy conservation in buildings is a paramount concern to combat greenhouse gas emissions and combat climate change. The efficient management of room occupancy, involving actions like lighting control and climate adjustment, is a pivotal strategy to curtail energy consumption. In contexts where surveillance technology isn't viable, non-intrusive sensors are employed to estimate room occupancy. In this study, we present a predictive framework for room occupancy that leverages a diverse set of machine learning models, with Random Forest consistently achieving the highest predictive accuracy. Notably, this dataset encompasses both temporal and spatial dimensions, revealing a wealth of information. Intriguingly, our framework demonstrates robust performance even in the absence of explicit temporal modeling. These findings underscore the remarkable predictive power of traditional machine learning models. The success can be attributed to the presence of feature redundancy, the simplicity of linear spatial and temporal patterns, and the advantages of high-frequency data sampling. While these results are compelling, it's essential to remain open to the possibility that explicitly modeling the temporal dimension could unlock deeper insights or further enhance predictive capabilities in specific scenarios. In summary, our research not only validates the effectiveness of our prediction framework for continuous and classification tasks but also underscores the potential for improvements through the inclusion of temporal aspects. The study highlights the promise of machine learning in shaping energy-efficient practices and room occupancy management.
American Journal of Applied Mathematics and Statistics. 2024, 12(1), 1-9. DOI: 10.12691/ajams-12-1-1
Pub. Date: January 22, 2024