Content deleted Content added
m →External links: Added a resource |
Jerry Hintze (talk | contribs) Added links to software that does this. |
||
Line 94:
One way to assess if the residuals from the Box–Jenkins model follow the assumptions is to generate [[statistical graphics]] (including an autocorrelation plot) of the residuals. One could also look at the value of the [[Ljung-Box test|Box–Ljung statistic]].
==Software Implementations==
Various packages that apply methodology like [[Box-Jenkins]] parameter optimization are available to find the right parameters for the ARIMA model.
* [[EViews]]: has extensive ARIMA and SARIMA capabilities.
* [[julia language|Julia]]: contains an ARIMA implementation in the TimeModels package<ref>https://github.com/JuliaStats/TimeModels.jl TimeModels.jl] www.github.com</ref>
* [[Mathematica]]: includes [http://reference.wolfram.com/mathematica/ref/ARIMAProcess.html ARIMAProcess] function.
* [[MATLAB]]: the [http://www.mathworks.com/products/econometrics/ Econometrics Toolbox] includes [http://www.mathworks.com/help/econ/arimaclass.html ARIMA models] and [http://www.mathworks.com/help/econ/regarimaclass.html regression with ARIMA errors]
* [[NCSS (statistical software)|NCSS]]: includes several procedures for <code>ARIMA</code> fitting and forecasting.<ref>[http://ncss.wpengine.netdna-cdn.com/wp-content/themes/ncss/pdf/Procedures/NCSS/ARIMA-Box-Jenkins.pdf ARIMA in NCSS],</ref><ref>[http://ncss.wpengine.netdna-cdn.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Automatic_ARMA.pdf Automatic ARMA in NCSS],</ref><ref>[http://ncss.wpengine.netdna-cdn.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Autocorrelations.pdf Autocorrelations and Partial Autocorrelations in NCSS]</ref>
* [[Python (programming language)|Python]]: the [https://pypi.python.org/pypi/statsmodels "statsmodels"] package includes models for time series analysis - univariate time series analysis: AR, ARIMA - vector autoregressive models, VAR and structural VAR - descriptive statistics and process models for time series analysis.
* [[R (programming language)|R]]: the standard R ''stats'' package includes an ''arima'' function, which is documented in [http://search.r-project.org/R/library/stats/html/arima.html "ARIMA Modelling of Time Series"]. Besides the ''ARIMA(p,d,q)'' part, the function also includes seasonal factors, an intercept term, and exogenous variables (''xreg'', called "external regressors"). The CRAN task view on [http://cran.r-project.org/web/views/TimeSeries.html Time Series] is the reference with many more links. The [http://cran.r-project.org/web/packages/forecast/index.html "forecast"] package in [[R (programming language)|R]] can automatically select an ARIMA model for a given time series with the auto.arima() function. The package can also simulate seasonal and non-seasonal ARIMA models with its simulate.Arima() function. It also has a function Arima(), which is a wrapper for the arima from the "stats" package.<ref>{{Cite web|url=https://www.otexts.org/fpp/8/7|title=8.7 ARIMA modelling in R {{!}} OTexts|website=www.otexts.org|access-date=2016-05-12}}</ref>
* [[Ruby (programming language)|Ruby]]: the [https://rubygems.org/gems/statsample-timeseries "statsample-timeseries"] gem is used for time series analysis, including ARIMA models and Kalman Filtering.
* [[SAS (software)|SAS]]: includes extensive ARIMA processing in its Econometric and Time Series Analysis system: SAS/ETS.
* IBM [[SPSS]]: includes ARIMA modeling in its Statistics and Modeler statistical packages. The default Expert Modeler feature evaluates a range of seasonal and non-seasonal autoregressive (''p''), integrated (''d''), and moving average (''q'') settings and seven exponential smoothing models. The Expert Modeler can also transform the target time-series data into its square root or natural log. The user also has the option to restrict the Expert Modeler to ARIMA models, or to manually enter ARIMA nonseasonal and seasonal ''p'', ''d'', and ''q'' settings without Expert Modeler. Automatic outlier detection is available for seven types of outliers, and the detected outliers will be accommodated in the time-series model if this feature is selected.
* [[SAP AG|SAP]]: the APO-FCS package<ref>{{cite web|title=Box Jenkins model|url=http://help.sap.com/saphelp_45b/helpdata/en/35/8a524b52060634e10000009b38f9b9/content.htm|publisher=SAP|accessdate=8 March 2013}}</ref> in [[SAP ERP]] from [[SAP AG|SAP]] allows creation and fitting of ARIMA models using the Box-Jenkins methodology.
* [[SQL Server Analysis Services]]: from [[Microsoft]] includes ARIMA as a Data Mining algorithm.
* [[Stata]] includes ARIMA modelling (using its arima command) as of Stata 9.
==References==
| |||