Módszerek összehasonlítása

	Koopa: Koopman-prediktorok nem-stacionárius idősorokhoz	DLinear: Dekompozíciós lineáris modell idősor-előrejelzéshez	Állapotterek (State Space) modell (Kalman-szűrő)
Tudományterület	Mélytanulás	Mélytanulás	Ökonometria
Módszercsalád	Machine learning	Machine learning	Regression model
Keletkezés éve	2023	2023	1990
Megalkotó	Yong Liu et al.	Ailing Zeng et al.	Harvey; Durbin & Koopman (state space treatment); Kalman filter
Típus	Koopman operator-based time-series forecasting model	Decomposition-based linear forecasting model	State space time series model
Alapmű	Liu, Y., Li, C., Wang, J., & Long, M. (2023). Koopa: Learning non-stationary time series dynamics with Koopman predictors. NeurIPS.	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.
Alternatív nevek	Koopman Predictor, Koopman-based Time-Series Model, Koopa Forecaster, Koopman Tahmincisi	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
Kapcsolódó	3	3	4
Összefoglaló	Koopa is a deep learning model for time-series forecasting introduced by Yong Liu, Chang Li, Jianmin Wang, and Mingsheng Long at NeurIPS 2023. It addresses the challenge of non-stationarity by disentangling time series into stationary and non-stationary components, then modeling the non-stationary dynamics using a learned approximation of the Koopman operator — a mathematical framework that lifts nonlinear systems into a linear space for tractable long-horizon prediction.	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.