Salīdzināt metodes

	DLinear: Dekompozīcijas lineārais modelis laika sēriju prognozēšanai	Valsts telpas modelis (Kalmana filtrs)
Nozare	Dziļā mācīšanās	Ekonometrija
Saime	Machine learning	Regression model
Izcelsmes gads	2023	1990
Autors	Ailing Zeng et al.	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Tips	Decomposition-based linear forecasting model	State space time series model
Pirmavots	Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
Citi nosaukumi	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Saistītās	3	4
Kopsavilkums	DLinear is a lightweight time series forecasting model introduced by Zeng et al. at AAAI 2023. It challenges the prevailing assumption that Transformer-based architectures are necessary for accurate long-horizon forecasting. The model decomposes an input sequence into trend and seasonal components using a moving average filter, then applies separate single-layer linear transformations to each component before summing their outputs to produce the final forecast.	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.