Salīdzināt metodes

	Autoformer: Transformer ar dekompozīciju ilgtermiņa laika virkņu prognozēšanai	Valsts telpas modelis (Kalmana filtrs)
Nozare	Dziļā mācīšanās	Ekonometrija
Saime	Machine learning	Regression model
Izcelsmes gads	2021	1990
Autors	Haixu Wu et al. (Tsinghua)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tips	Decomposition-based deep forecasting model	State space time series model
Pirmavots	Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. NeurIPS, 34.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Citi nosaukumi	Auto-Correlation Transformer, Decomposition Transformer, Series Decomposition Forecaster, Oto-Korelasyon Ayrışım Transformer	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Saistītās	4	4
Kopsavilkums	Autoformer is a deep learning architecture for long-term time-series forecasting, introduced by Wu et al. from Tsinghua University at NeurIPS 2021. It replaces the standard self-attention mechanism with an Auto-Correlation mechanism that exploits periodic dependencies in the frequency domain, and embeds a progressive series decomposition block throughout the encoder and decoder to separately model trend and seasonal components.	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.