เปรียบเทียบวิธี

	Prophet	แบบจำลองปริภูมิสถานะ (ตัวกรองคาลมาน)
สาขาวิชา	เศรษฐมิติ	เศรษฐมิติ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2018	1990
ผู้ริเริ่ม	Taylor & Letham (Facebook/Meta)	Harvey; Durbin & Koopman (state space treatment); Kalman filter
ประเภท	Decomposable (structural) time series model	State space time series model
แหล่งต้นตำรับ	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.	Harvey, A. C. (1990). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press.
ชื่อเรียกอื่น	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale	state space, Kalman filter, unobserved components model, Durum Uzayı Modeli (State Space / Kalman Filter)
ที่เกี่ยวข้อง	5	4
สรุป	Prophet is a Bayesian structural time series model introduced by Taylor and Letham at Facebook/Meta in 2018. It forecasts a continuous series by decomposing it into separate, interpretable trend, seasonality, and holiday components, and is designed to be approachable for analysts working at scale.	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.