Compara mètodes

	DLinear: Model Lineal de Descomposició per a la Predicció de Sèries Temporals	Model d'ARIMA (Autoregressive Integrated Moving Average)
Camp	Aprenentatge profund	Econometria
Família	Machine learning	Regression model
Any d'origen	2023	2015
Autor original	Ailing Zeng et al.	Box & Jenkins (Box-Jenkins methodology)
Tipus	Decomposition-based linear forecasting model	Univariate time-series model
Font seminal	Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? AAAI.	Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley. ISBN: 978-1118675021
Àlies	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Relacionats	3	5
Resum	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.	ARIMA is a univariate time-series forecasting model that combines autoregressive, integrated (differencing), and moving-average components to predict a single continuous series from its own past. It is the centrepiece of the Box-Jenkins methodology set out in Box, Jenkins, Reinsel & Ljung's Time Series Analysis (5th ed., 2015).