手法を比較

	Crossformer: 多変量時系列予測のためのクロスディメンション依存性トランスフォーマー	Informer	iTransformer: 多変量時系列予測のための逆転Transformer
分野	深層学習	深層学習	深層学習
系統	Machine learning	Machine learning	Machine learning
提唱年	2023	2021	2024
提唱者	Yunhao Zhang & Junchi Yan	Zhou, H. et al.	Yong Liu et al.
種類	Transformer-based multivariate time-series forecasting model	Transformer (ProbSparse self-attention)	Inverted-attention sequence model
原典	Zhang, Y., & Yan, J. (2023). Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. ICLR.	Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.	Liu, Y., Hu, T., Zhang, H., Wu, H., Wang, S., Ma, L., & Long, M. (2024). iTransformer: Inverted transformers are effective for time series forecasting. ICLR.
別名	Cross-Dimension Dependency Transformer, Crossformer TSF, Çapraz-Boyut Bağımlılık Transformatörü	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster	Inverted Transformer, iTransformer for Time Series, Inverted Attention Transformer, Ters Transformer
関連	3	5	2
概要	Crossformer is a Transformer-based architecture for multivariate time series forecasting, introduced by Yunhao Zhang and Junchi Yan at ICLR 2023. Unlike earlier Transformer variants that treat each variate independently, Crossformer explicitly models cross-dimension dependencies alongside temporal patterns. It achieves this through a two-stage attention design — cross-time and cross-dimension — applied over segment-level embeddings organized in a hierarchical encoder, enabling the model to capture both intra-variate dynamics and inter-variate correlations simultaneously.	Informer is a Transformer-based model introduced by Zhou et al. in 2021 for long-sequence time-series forecasting, using a ProbSparse self-attention mechanism that lowers the computational complexity of the standard Transformer to O(L log L). It is built for problems that demand predictions across thousands of future steps.	iTransformer is a deep-learning architecture for multivariate time-series forecasting introduced by Liu et al. at ICLR 2024. Its defining idea is to invert the conventional Transformer tokenisation strategy: instead of treating each time step as a token, iTransformer treats each variate (sensor channel or feature series) as a single token whose embedding encodes the full observed look-back window. Self-attention is then applied across variates to capture inter-series dependencies, while a feed-forward network within each token learns temporal patterns.