Two-dimensional attention-based multi-input LSTM for time series prediction-Reference-Cited by-同舟云学术

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Two-dimensional attention-based multi-input LSTM for time series prediction

Published:2021-01-31 Issue:1 Volume:28 Page:39-57
ISSN:2383-4757
Container-title:Communications for Statistical Applications and Methods
language:en
Short-container-title:CSAM

Author:

Kim Eun Been¹,Park Jung Hoon¹,Lee Yung-Seop,Lim Changwon¹

Affiliation:

1. Department of Applied Statistics, Chung-Ang University, Korea

Funder

Ministry of Science, ICT

Publisher

Communications for Statistical Applications and Methods

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Finance,Modeling and Simulation,Statistics and Probability

Reference25 articles.

1. Data driven prediction models of energy use of appliances in a low-energy house

2. NARX-Based Nonlinear System Identification Using Orthogonal Least Squares Basis Hunting

3. Cho K, van Merriënboer B, Bahdanau D, and Bengio Y (2014a). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.1409.1259

4. Cho K, van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, and Bengio Y (2014b). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.1406.1078

5. Distributed representations, simple recurrent networks, and grammatical structure

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1. Application of technology maturity assessment and achievement selection based on LSTM time series prediction;International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023);2023-12-07

2. A state of art review on time series forecasting with machine learning for environmental parameters in agricultural greenhouses;Information Processing in Agriculture;2022-10

3. Wavelet‐attention‐based traffic prediction for smart cities;IET Smart Cities;2021-11-25

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