手法を比較

	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
系統	Machine learning	Machine learning
提唱年	2021	2024
提唱者	Zhou, H. et al.	Yong Liu et al.
種類	Transformer (ProbSparse self-attention)	Inverted-attention sequence model
原典	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	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster	Inverted Transformer, iTransformer for Time Series, Inverted Attention Transformer, Ters Transformer
関連	5	2
概要	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.