পদ্ধতির তুলনা করুন

	ARIMA (Autoregressive Integrated Moving Average) মডেল	ব্যাহত সময় সিরিজ (Interrupted Time Series - ITS) বিশ্লেষণ	মার্কভ চেইন মন্টি কার্লো (MCMC)
ক্ষেত্র	অর্থমিতি	কার্যকারণ অনুমান	বেইসীয়
পরিবার	Regression model	Regression model	Bayesian methods
উদ্ভবের বছর	2015	2002	—
প্রবর্তক	Box & Jenkins (Box-Jenkins methodology)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)	—
ধরন	Univariate time-series model	Quasi-experimental segmented regression	Posterior sampling algorithm
মৌলিক উৎস	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	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
অপর নাম	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi	markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo)
সম্পর্কিত	5	5	3
সারসংক্ষেপ	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).	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.	Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model.