Confronta i metodi

	DLinear: Modello Lineare a Decomposizione per la Previsione di Serie Storiche	Modello ARIMA (Autoregressive Integrated Moving Average)
Campo	Apprendimento profondo	Econometria
Famiglia	Machine learning	Regression model
Anno di origine	2023	2015
Ideatore	Ailing Zeng et al.	Box & Jenkins (Box-Jenkins methodology)
Tipo	Decomposition-based linear forecasting model	Univariate time-series model
Fonte seminale	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
Alias	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
Correlati	3	5
Sintesi	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).