手法を比較

	MICN: 長期時系列予測のためのマルチスケール等長畳み込みネットワーク	SCINet: 時系列予測のためのサンプル畳み込みおよび相互作用ネットワーク
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2023	2022
提唱者	Huiqiang Wang et al.	Minhao Liu et al.
種類	CNN-based time-series forecasting architecture	Hierarchical convolutional time-series forecasting network
原典	Wang, H., Peng, J., Huang, F., Wang, J., Chen, J., & Xiao, Y. (2023). MICN: Multi-scale local and global context modeling for long-term series forecasting. ICLR.	Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). SCINet: Time series modeling and forecasting with sample convolution and interaction. NeurIPS.
別名	Multi-scale Isometric Convolution Network, Multi-scale Local and Global Context Model, MICN Forecaster, Çok Ölçekli İzometrik Evrişim Ağı	Sample Convolution and Interaction Network, SCI-Net, Temporal Downsampling Convolution Network, Örneklem Evrişim ve Etkileşim Ağı
関連	2	2
概要	MICN (Multi-scale Isometric Convolution Network) is a convolutional neural network architecture for long-term time-series forecasting introduced by Huiqiang Wang and colleagues at ICLR 2023. Its central idea is to capture both local temporal patterns and global seasonal dependencies simultaneously through multi-scale isometric convolutions combined with a merge attention mechanism, enabling efficient and expressive modeling of complex temporal dynamics without the quadratic cost of full self-attention.	SCINet is a deep learning architecture for multi-step time-series forecasting introduced by Liu et al. at NeurIPS 2022. Its core idea is a recursive binary-tree structure of SCI-Blocks, each of which splits an input sequence into odd- and even-indexed sub-sequences, applies convolutional filters to model cross-subsequence interactions, and then merges the learned representations. This hierarchical downsampling strategy enables the network to capture temporal dependencies at multiple resolutions simultaneously.