Modeling Time Series Data with Deep Learning: A Review, Analysis, Evaluation and Future Trend

Citation

Ng, Kok Why and Chua, Fang Fang and Ang, John Syin (2020) Modeling Time Series Data with Deep Learning: A Review, Analysis, Evaluation and Future Trend. In: 2020 8th International Conference on Information Technology and Multimedia (ICIMU), 24-26 Aug. 2020, Selangor, Malaysia.

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Abstract

Time series modeling is a challenging and demanding problem. In the recent year, deep learning (DL) has attracted huge attention in many fields of research, including time series analysis and forecasting. While the methods of DL are very broad and wide, we aim to review the most recent and impactful deep learning papers in order to provide insights from the notable DL models and evaluation methods on time series problems. Our main objective is to review and analyse the advantages and disadvantages of different models, evaluation methods, future trends and techniques of solving time series problem with DL.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Convolutional Neural Network
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 20 Oct 2021 02:25
Last Modified: 20 Oct 2021 02:25
URII: http://shdl.mmu.edu.my/id/eprint/8301

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