Abstract
This paper presents a thorough exploration of time series analysis within the broader landscape of machine learning and deep learning. From fundamental principles such as linear modeling to more complex neural network structures, the paper navigates the evolving terrain of predictive modeling. It discusses the amalgamation of various methodologies within machine learning and deep learning, addressing challenges related to interpretability, ethical considerations, and biases inherent in training datasets. Emerging trends, including the wider accessibility of methodologies through open-source tools and a growing emphasis on transparency, are brought to light. Looking ahead, the paper envisions ongoing innovation in time series analysis, incorporating diverse approaches such as reinforcement learning, federated learning, and domain-specific knowledge. It underscores the need to address ethical concerns and biases for a future where predictive analytics is not only accurate but also ethically grounded and universally applicable.
Original language | English |
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Title of host publication | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 251-256 |
Number of pages | 6 |
ISBN (Electronic) | 9798350374100 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 - Bhopal, India Duration: 24 Feb 2024 → 25 Feb 2024 |
Publication series
Name | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 |
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Conference
Conference | 2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 |
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Country/Territory | India |
City | Bhopal |
Period | 24/02/24 → 25/02/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- CNN
- Deep Learning
- Linear Regression
- Machine Learning