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An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains

Corrigan, Owen ORCID: 0000-0002-1840-982X (2018) An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains. PhD thesis, Dublin City University.

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Abstract

In this thesis we will examine architectures and models for machine learning in three problem domains each of which are based around the use of time series data in time series applications. We set out to examine whether the architecture and model solutions in different problem domains will converge when optimised towards a similar solution or not. Stated clearly, our central research question is “That problem-solving in diverse problem domains using Machine Learning applied to time series data requires diverse models in order to achieve the best performance” . To investigate this research hypothesis we use a case study methodology. We will investigate three separate and diverse problem domains, and compare their results and best solutions. The first problem domain is in the field of educational analytics, the second is in the field of agri-analytics and the third is in the field of environmental science.

Item Type:Thesis (PhD)
Date of Award:January 2018
Refereed:No
Supervisor(s):Smeaton, Alan F.
Subjects:Computer Science > Machine learning
Computer Science > Artificial intelligence
Computer Science > Image processing
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:Science Foundation Ireland grant number 12/RC/2289
ID Code:22179
Deposited On:05 Apr 2018 09:09 by Alan Smeaton . Last Modified 13 Aug 2020 15:20

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