Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10352
Title: Time series prediction with hierarchical recurrent model
Authors: Keskin, Mustafa Mert
İrim, Fatih
Karaahmetoglu, Oguzhan
Kaya, Ersin
Keywords: Time series prediction
Recurrent neural networks
RNN
LSTM
Lstm
Cnn
Issue Date: 2023
Publisher: Springer London Ltd
Abstract: In this paper, we investigate the capability of modeling distant temporal interaction of Long Short-Term Memory (LSTM) and introduce a novel Long Short-Term Memory on time series problems. To increase the capability of modeling distant temporal interactions, we propose a hierarchical architecture (HLSTM) using several LSTM models and a linear layer. This novel framework is then applied to electric power consumption, real-life crime and financial data. We demonstrate in our simulations that this structure significantly improves the modeling of deep temporal connections compared to the classical architecture of LSTM and various studies in the literature. Furthermore, we analyze the sensitivity of the new architecture with respect to the hidden size of LSTM.
URI: https://doi.org/10.1007/s11760-022-02426-6
https://hdl.handle.net/20.500.11851/10352
ISSN: 1863-1703
1863-1711
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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