Linganisha mbinu

	FEDformer: Transformer Iliyoimarishwa kwa Masafa na Uozo	Mfumo wa Nafasi ya Hali (Kichujio cha Kalman)
Nyanja	Ujifunzaji wa Kina	Ekonometriki
Familia	Machine learning	Regression model
Mwaka wa asili	2022	1990
Mwanzilishi	Tian Zhou et al.	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Aina	Frequency-domain decomposed Transformer for time-series forecasting	State space time series model
Chanzo asilia	Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting. ICML.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Majina mbadala	Frequency Enhanced Decomposed Transformer, FED-Transformer, Frequency Domain Transformer, Frekans Tabanlı Ayrıştırılmış Dönüştürücü	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Zinazohusiana	3	4
Muhtasari	FEDformer is a Transformer-based architecture for long-term multivariate time-series forecasting, introduced by Zhou et al. at ICML 2022. Its core innovation is the combination of seasonal-trend decomposition with frequency-domain attention: instead of computing full token-to-token attention in the time domain, FEDformer projects queries, keys, and values into the frequency domain via Fourier or wavelet transforms and operates on a randomly selected subset of frequency components, achieving linear complexity while preserving global temporal structure.	A state space model is a general time series framework that describes a series through unobserved (latent) state variables linked by a measurement equation and a transition equation, with the states estimated in real time by the Kalman filter. Developed in the state space tradition of Harvey (1990) and Durbin & Koopman (2012), it nests ARIMA and exponential smoothing as special cases.