השוואת שיטות

	מְיַדֵּעַ	iTransformer: טרנספורמר מהופך לחיזוי סדרות זמן מרובות משתנים
תחום	למידה עמוקה	למידה עמוקה
משפחה	Machine learning	Machine learning
שנת המקור	2021	2024
הוגה השיטה	Zhou, H. et al.	Yong Liu et al.
סוג	Transformer (ProbSparse self-attention)	Inverted-attention sequence model
מקור מכונן	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.
כינויים	Informer — Uzun Dizi Transformer Tahmini, Informer transformer, ProbSparse attention forecaster	Inverted Transformer, iTransformer for Time Series, Inverted Attention Transformer, Ters Transformer
קשורות	5	2
תקציר	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.