:: (Partial) Autocorrelation Function - Free Statistics Software (Calculator) ::
All rights reserved. The non-commercial (academic) use of this software is free of charge. The only thing that is asked in return is to cite this software when results are used in publications.
This free online software (calculator) computes the autocorrelation and partial autocorrelation function for any univariate time series.
The parameters lambda, d, D, and seasonality are used to apply a Box-Cox transformation and (non-)seasonal differencing in order to induce stationarity of the time series. If lambda = 1, d = 0, and D = 0 then no transformation/differencing is applied before the (P)ACF is computed.
The confidence interval can be computed in two different ways:
- assuming a white noise time series (CI type = White Noise)
- assuming that the series is a MA(k-1) process when the CI of ACF(k) is computed (CI type = MA)
When you use this software to check the residuals of a time series model, CI type = White Noise is appropriate. In all other cases, setting the CI type = MA may be less misleading. Note that the CI of the PACF always assumes white noise.
Enter (or paste) your data delimited by hard returns.
Click here to edit the underlying code of this R Module.
|Cite this software as:|
|Wessa P., (2015), (Partial) Autocorrelation Function (v1.0.12) in Free Statistics Software (v1.1.23-r7), Office for Research Development and Education, URL http://www.wessa.net/rwasp_autocorrelation.wasp/|
|The R code is based on :|
|Borghers, E, and P. Wessa, Statistics - Econometrics - Forecasting, Office for Research Development and Education, http://www.xycoon.com/|
To cite Wessa.net in publications use:
Wessa, P. (2017), Free Statistics Software, Office for Research Development and Education,
version 1.1.23-r7, URL http://www.wessa.net/
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Software Version : 1.1.23-r7
Algorithms & Software : Patrick Wessa, PhD
Server : wessa.net