Bandingkan kaedah

	Model ARIMA (Autoregresif Bersepadu Purata Bergerak)	Ujian Stasioneriti KPSS	Ujian Punca Unit Phillips-Perron (PP)
Bidang	Ekonometrik	Ekonometrik	Ekonometrik
Keluarga	Regression model	Regression model	Regression model
Tahun asal	2015	1992	1988
Pengasas	Box & Jenkins (Box-Jenkins methodology)	Kwiatkowski, Phillips, Schmidt & Shin	Peter C. B. Phillips & Pierre Perron
Jenis	Univariate time-series model	Stationarity test (reverse of unit-root tests)	Unit-root test for stationarity
Sumber perintis	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	Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1–3), 159–178.	Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346.
Alias	Box-Jenkins model, ARIMA(p,d,q), ARIMA Modeli	Kwiatkowski-Phillips-Schmidt-Shin test, stationarity test, KPSS durağanlık testi	PP test, Phillips-Perron unit root test, Phillips-Perron birim kök testi
Berkaitan	5	4	4
Ringkasan	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).	The KPSS test, introduced by Kwiatkowski, Phillips, Schmidt and Shin in 1992, tests the null hypothesis that a series is stationary against the alternative that it contains a unit root — the reverse of the ADF and Phillips-Perron tests. By flipping the burden of proof, it is designed to be used alongside unit-root tests so that the two can confirm one another and expose ambiguous, borderline cases.	The Phillips-Perron test, proposed by Peter Phillips and Pierre Perron in 1988, tests for a unit root in a time series, like the Augmented Dickey-Fuller test, but corrects for autocorrelation and heteroskedasticity in the errors non-parametrically rather than by adding lagged differences. It runs a simple Dickey-Fuller regression and then adjusts the test statistic using a long-run variance estimate, so the practitioner need not choose a lag length for the regression itself.