Machine Learning and Deep Learning Algorithms in Times Series Analysis

Fatih Er*, Ibraheem Shayea, Bilal Saoud, Leila Rzayeva, Aigerim Alibek

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages251-256
Number of pages6
ISBN (Electronic)9798350374100
DOIs
Publication statusPublished - 2024
Event2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024 - Bhopal, India
Duration: 24 Feb 202425 Feb 2024

Publication series

Name2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

Conference

Conference2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024
Country/TerritoryIndia
CityBhopal
Period24/02/2425/02/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • CNN
  • Deep Learning
  • Linear Regression
  • Machine Learning

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