পদ্ধতির তুলনা করুন

	প্রফেট	সাধারণ ন্যূনতম বর্গক্ষেত্র (OLS) রিগ্রেশন
ক্ষেত্র	অর্থমিতি	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	2018	2019
প্রবর্তক	Taylor & Letham (Facebook/Meta)	Wooldridge (textbook treatment); classical least squares
ধরন	Decomposable (structural) time series model	Linear regression
মৌলিক উৎস	Taylor, S. J. & Letham, B. (2018). Forecasting at Scale. The American Statistician, 72(1), 37-45.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
অপর নাম	Prophet, Facebook Prophet, Meta Prophet, forecasting at scale	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
সম্পর্কিত	5	5
সারসংক্ষেপ	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.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).