So sánh phương pháp

	iTransformer: Transformer Nghịch đảo cho Dự báo Chuỗi Thời gian Đa biến	Crossformer
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2024	2023
Người khởi xướng	Yong Liu et al.	Yunhao Zhang & Junchi Yan
Loại	Inverted-attention sequence model	Transformer-based multivariate time-series forecasting model
Công trình gốc	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.	Zhang, Y., & Yan, J. (2023). Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting. ICLR.
Tên gọi khác	Inverted Transformer, iTransformer for Time Series, Inverted Attention Transformer, Ters Transformer	Cross-Dimension Dependency Transformer, Crossformer TSF, Çapraz-Boyut Bağımlılık Transformatörü
Liên quan	2	3
Tóm tắt	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.	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.