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: the APO-FCS package in SAP ERP from 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. StatSim: includes ARIMA models in the Forecast web app. Teradata Vantage has the ARIMA function as part of its machine learning engine. TOL (Time Oriented Language) is designed to model ARIMA models (including SARIMA, ARIMAX and DSARIMAX variants) . Scala: spark-timeseries library contains ARIMA implementation for Scala, Java and Python. Implementation is designed to run on Apache Spark. PostgreSQL/MadLib: Time Series Analysis/ARIMA. X-12-ARIMA: from the US Bureau of the Census See also Autocorrelation ARMA Partial autocorrelation Finite impulse response Infinite impulse response References Further reading External links The US Census Bureau uses ARIMA for "seasonally adjusted" data (programs, docs, and papers here) Lecture notes on ARIMA models (Robert Nau, Duke University) Time series models de:ARMA-Modell#ARIMA