MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting

By Huiqiang Wang, Jian Peng, Feihu Huang, Jince Wang, Junhui Chen, Yifei Xiao. International Conference on Learning Representations, 2023

600 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/6f370b047624dafea4e59df20ac4cbec538dc44a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Shiyu Wang
2. Donghao Luo
3. Xiaoming Shi
4. Xue Wang
5. Tengge Hu
6. Lintao Ma
7. Huakun Luo
8. James Y. Zhang

Citations in the corpus

• ModernTCN: A Modern Pure Convolution Structure for General Time Series Analysis (2024, background): “Since a longer input length indicates more historical information an algorithm can utilize in time series forecasting, a model with strong ability to capture long-term temporal dependency should perform better when input length increases (Zeng et al., 2022; Wang et al., 2023; Nie et al., 2023).”
• TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting (2024, methodology): “According to foundation backbones, deep models can be roughly categorized into the following four paradigms: RNN-, CNN-, Transformer-and MLP-based methods.”