השוואת שיטות

	DLinear: מודל לינארי מפורק לחיזוי סדרות עתיות	מודל ARIMA (Autoregressive Integrated Moving Average)
תחום	למידה עמוקה	אקונומטריקה
משפחה	Machine learning	Regression model
שנת המקור	2023	2015
הוגה השיטה	Ailing Zeng et al.	Box & Jenkins (Box-Jenkins methodology)
סוג	Decomposition-based linear forecasting model	Univariate time-series model
מקור מכונן	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
כינויים	Decomposition Linear, DLinear Forecaster, Linear Decomposition Model, Ayrışım Doğrusal Modeli	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli
קשורות	3	5
תקציר	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).