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An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains
Corrigan, Owen
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.
Keyword(s): Machine learning; Artificial intelligence; Image processing
Publication Date:
2018
Type: Doctoral thesis
Peer-Reviewed: No
Language(s): English
Institution: Dublin City University
Citation(s): Corrigan, Owen (2018) An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains. PhD thesis, Dublin City University.
Publisher(s): Dublin City University. INSIGHT Centre for Data Analytics; Dublin City University. School of Computing
File Format(s): application/pdf
Supervisor(s): Smeaton, Alan F.
Related Link(s): http://doras.dcu.ie/22179/1/owen_corrigan_thesis.pdf
First Indexed: 2018-04-06 06:05:02 Last Updated: 2018-04-10 06:05:05