Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data-Reference-Cited by-同舟云学术

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Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data

Published:2023-12 Issue:12 Volume:45 Page:14266-14283
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Sim Sunghyun¹^ORCID,Kim Dohee²^ORCID,Bae Hyerim²^ORCID

Affiliation:

1. Industrial Management and Big Data Engineering Major Division of Industrial Convergence System Engineering, Dongeui University, Busan, South Korea

2. Industrial Data Science and Engineering, Department of Industrial Engineering, Pusan National University, Busan, South Korea

Funder

Ministry of Science and ICT, South Korea

Information Technology Research Center support program

Institute for Information & communications Technology Planning & Evaluation

National Research Foundataion of Korea

Korea governmen

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10308548/10264112.pdf?arnumber=10264112

Reference40 articles.

1. A Comparison of ARIMA and LSTM in Forecasting Time Series

2. Temporal pattern attention for multivariate time series forecasting

3. Gating revisited: Deep multi-layer RNNsthat can be trained;turkoglu;IEEE Trans Pattern Anal Mach Intell,2022

4. Effective passenger flow forecasting using STL and ESN based on two improvement strategies

5. DeepAR: Probabilistic forecasting with autoregressive recurrent networks

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

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