AI-Enhanced Financial Forecasting
Keywords:
AI-Enhanced, FinancialAbstract
There is a critical interface in the scientific field of financial forecasting, encompassing machine learning and artificial intelligence. Who would not be interested in knowing what the next few years might hold in terms of economic development, asset prices, or revenue expectations? Be it trading, investment, or corporate management, stakeholders strongly depend on the accuracy, sensitivity, and specificity of accurate future predictions. Therefore, next-year and multi-year forecasts guide different future decisions; thus, the predictability and extent of predictability are equally important aspects in financial forecasting. Hence, the capabilities of methods and their predictions are relatively comparable. Despite the increasing computing power and fast memory available, this measure has not shown a clear trend towards increased predictability. There is more evidence that the amplitude of the unexpected deviation is relatively unchanged. In fact, the standard deviation indicates that the size of the unexpected deviation has only slightly increased in the last 30 years. There were only two years in that period which showed a higher amplitude than the previous 29 years.
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Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.
Pasupuleti, Vikram, et al. "Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management." Logistics 8.3 (2024): 73.
Thota, Shashi, et al. "Federated Learning: Privacy-Preserving Collaborative Machine Learning." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 168-190.
J. Singh, “Advancements in AI-Driven Autonomous Robotics: Leveraging Deep Learning for Real-Time Decision Making and Object Recognition”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 657–697, Apr. 2023
Alluri, Venkat Rama Raju, et al. "Serverless Computing for DevOps: Practical Use Cases and Performance Analysis." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 158-180.
Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
S. Chitta, S. Thota, S. Manoj Yellepeddi, A. Kumar Reddy, and A. K. P. Venkata, “Multimodal Deep Learning: Integrating Vision and Language for Real-World Applications”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 262–282, Nov. 2020
Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.
Tamanampudi, Venkata Mohit. "CoWPE: Adaptive Context Window Adjustment in LLMs for Complex Input Queries." Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023 5.1 (2024): 438-450.
Thota, Shashi, et al. "Few-Shot Learning in Computer Vision: Practical Applications and Techniques." Human-Computer Interaction Perspectives 3.1 (2023): 29-59.
Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.
J. Singh, “Autonomous Vehicle Swarm Robotics: Real-Time Coordination Using AI for Urban Traffic and Fleet Management”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 1–44, Aug. 2023
S. Kumari, “Cloud Transformation for Mobile Products: Leveraging AI to Automate Infrastructure Management, Scalability, and Cost Efficiency”, J. Computational Intel. & Robotics, vol. 4, no. 1, pp. 130–151, Jan. 2024.
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