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Thesis

Deep learning for time series prediction and decision making over time

Abstract:

In this thesis, we develop a collection of state-of-the-art deep learning models for time series forecasting. Primarily focusing on a closer alignment with traditional methods in time series modelling, we adopt three main directions of research -- 1) novel architectures, 2) hybrid models, and 3) feature extraction. Firstly, we propose two new architectures for general one-step-ahead and multi-horizon forecasting. With the Recurrent Neural Filter (RNF), we take a closer look at the relation...

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Division:
MPLS
Department:
Engineering Science
Role:
Author

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Role:
Supervisor
Role:
Supervisor
Role:
Examiner
ORCID:
0000-0002-1143-9786
Role:
Examiner
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Programme:
Research Studentship - Oxford Man Institute
Funding agency for:
Lim, B
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford
Language:
English
Keywords:
Subjects:
Deposit date:
2020-12-24

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