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4. Exploiting Data Science for Measuring the Performance of Technology Stocks.

Author

Sher, Tahir, Rehman, Abdul, Dongsun Kim, and Ihsan, Imran

Subjects
SUPERVISED learning, DATA science, PERFORMANCE technology, FUTURES, STOCKS (Finance), MACHINE learning
Abstract

The rise or fall of the stock markets directly affects investors' interest and loyalty. Therefore, it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses. In our proposed study, six supervised machine learning (ML) strategies and deep learning (DL) models with long short-term memory (LSTM) of data science was deployed for thorough analysis and measurement of the performance of the technology stocks. Under discussion are Apple Inc. (AAPL), Microsoft Corporation (MSFT), Broadcom Inc., Taiwan Semiconductor Manufacturing Company Limited (TSM), NVIDIA Corporation (NVDA), and Avigilon Corporation (AVGO). The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022 (seventeen years) with 4280 samples. As already noted, multiple studies have been performed to resolve this problem using linear regression, support vectormachines, deep long short-termmemory (LSTM), and many other models. In this research, the Hidden Markov Model (HMM) outperformed other employed machine learning ensembles, tree-basedmodels, the ARIMA (Auto Regressive IntegratedMoving Average)model, and long short-term memory with a robust mean accuracy score of 99.98. Other statistical analyses and measurements for machine learning ensemble algorithms, the Long Short-TermModel, and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data. Thus, the proposed research found the best model to be HMM, and LSTM was the second-best model that performed well in all aspects. A developedmodel will be highly recommended and helpful for earlymeasurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Moving From Problem Space to Solution Space

Author

Raja, Bilal Saeed, M. Ali Iqbal, and Ihsan, Imran

Subjects
Functional Requirements, Non Functional Requirements, Twin Peaks Model, QARCC
Abstract

Extracting and elaborating software requirements and transforming them into viable software architecture are still an intricate task. This paper defines a solution architecture which is based on the blurred amalgamation of problem space and solution space. The dependencies between domain constraints, requirements and architecture and their importance are described that are to be considered collectively while evolving from problem space to solution space. This paper proposes a revised version of Twin Peaks Model named Win Peaks Model that reconciles software requirements and architecture in more consistent and adaptable manner. Further the conflict between stakeholders- win-requirements is resolved by proposed Voting methodology that is simple adaptation of win-win requirements negotiation model and QARCC., {"references":["Standish Research Paper, Chaos Study, 1995, available on-line at\nhttp://www.standishgroup.com .","B. Paech, A.H. Detroit, D. Kerkow and A. von Knethen, \"Functional\nrequirements, non-functional requirements, and architecture should not\nbe separated - A position paper\". REFSQ' 2002, Essen, Germany,\nSeptember 9-10 2002.","Bashar Nuselbeh, \"Weaving Together Requirements and Architectures\",\nin proceedings of IEEE Computer, 34, 3 (March 2001), pp. 115-119.","Paul Gr├╝nbacher, Alexander Egyed, and Nenad Medvidovic.\n\"Reconciling Software Requirements and Architectures: The CBSP\nApproach.\" in Proceedings of the Fifth IEEE International Symposium\non Requirement Engineering, 2001.","Boehm, Barry \"Conflict Analysis and Negotiation Aids for Cost-Quality\nRequirements\", Center for Software Engineering and Computer Science\nDepartment, University of Southern California, 1999.","Hall, J.G.; Jackson, M.; Laney, R.C.; Nuseibeh, B.; Rapanotti, L.\n\"Relating software requirements and architectures using problem\nframes\". Proceedings of the IEEE Joint International Requirements\nEngineering Conference (RE'02), Essen, Germany, 9-13 September,\n2002, Pages: 137- 144.","Boehm, B. and Egyed, A., \"Software Requirements Negotiation: Some\nLessons Learned\", in proceedings of the 20th International Conference\non Software Engineering, April 1998.","Alexander Egyed, Barry Boehm, \"Analysis of System Requirements\nNegotiation Behavior Patterns\". Proceedings of 7th Annual\nInternational Symposium on Systems Engineering, 1997.","Robert Glass, \"Software Runaways: Lessons Learned from Massive\nProject Failure\" published in the magazine Computerworld, 1989.\n[10] Steve McConnell, \"Software Project Survival Guide\" a book published\nby Microsoft Press, 1998.\n[11] Awais Rashid , Ana Moreira , Jo─üo Ara├║jo, \"Modularisation and\ncomposition of aspectual requirements\", Proceedings of the 2nd\ninternational conference on Aspect-oriented software development, p.11-\n20, March 17-21, 2003, Boston, Massachusetts.\n[12] Hall J.G., Rapanotti, L., \"A reference model for requirements\nengineering\", in IEEE Proceedings of International Requirements\nEngineering Conference (RE'03), USA, September 2003."]}

Published
2007
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