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Title: Time series prediction with hierarchical recurrent model
Authors: Keskin, Mustafa Mert
Irım, Fatih
Karaahmetoğlu, Oğuzhan
Kaya, Ersin
Keywords: LSTM
Recurrent neural networks
Time series prediction
Electric utilities
Memory architecture
Network architecture
Time series
Crime data
Electric power consumption
Financial data
Hierarchical architectures
Memory modeling
Recurrent models
Time series prediction
Times series
Long short-term memory
Issue Date: 2022
Publisher: Springer Science and Business Media Deutschland GmbH
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. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
ISSN: 1863-1703
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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