Temporal Attention Gate Network With Temporal Decomposition for Improved Prediction Accuracy of Univariate Time-Series Data-Reference-Cited by-同舟云学术

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Temporal Attention Gate Network With Temporal Decomposition for Improved Prediction Accuracy of Univariate Time-Series Data

Published:2023-02-20 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)
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Author:

Sim Sunghyun¹,Kim Dohee²,Jeong Seok Chan³

Affiliation:

1. Dong-Eui University,Industrial Management & Big Data Engineering Major, Div. of Industrial Convergence System Engineering,Busan,South Korea

2. Pusan National University,Industrial Data Science & Engineering Major,Dept. of Industrial Engineering,Busan,South Korea

3. Dong-Eui University,College of Commerce and Economics & AI Grand ICT Research Center,Dept. of e-Business

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10066849/10066958/10067135.pdf?arnumber=10067135

Reference21 articles.

1. Variational Mode Decomposition

2. Short-Term PV Power Prediction Based on Optimized VMD and LSTM

3. Sequence to sequence weather forecasting with long short-term memory recurrent neural networks;zaytar;Int J Comput Appl,2016

4. A State-of-the-Art Survey on Deep Learning Theory and Architectures

5. Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads

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1. A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection;The Journal of Engineering;2024-07

2. Temporal Multi-Features Representation Learning-Based Clustering for Time-Series Data;IEEE Access;2024

3. Improving Time-Series Classification Accuracy Based on Temporal Feature Representation Learning Using CRU-LSTM Autoencoder;2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI);2023-11-01

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