השוואת שיטות

	מודל ARIMA (Autoregressive Integrated Moving Average)	רגרסיה מקומית LOESS / LOWESS
תחום	אקונומטריקה	למידת מכונה
משפחה	Regression model	Machine learning
שנת המקור	2015	1979
הוגה השיטה	Box & Jenkins (Box-Jenkins methodology)	William S. Cleveland
סוג	Univariate time-series model	Local nonparametric regression smoother
מקור מכונן	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	Cleveland, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368), 829–836.
כינויים	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	LOWESS, local regression, locally weighted scatterplot smoothing, yerel regresyon
קשורות	5	3
תקציר	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).	LOESS (locally estimated scatterplot smoothing), introduced by William Cleveland in 1979 and extended with Susan Devlin in 1988, fits a smooth curve through data by performing a separate weighted polynomial regression in the neighbourhood of each point. Nearby observations count more than distant ones, so the method follows local structure without assuming any global functional form, making it a popular exploratory smoother for scatterplots.