The ARIMA Procedure
- Overview
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Getting Started
The Three Stages of ARIMA ModelingIdentification StageEstimation and Diagnostic Checking StageForecasting StageUsing ARIMA Procedure StatementsGeneral Notation for ARIMA ModelsStationarityDifferencingSubset, Seasonal, and Factored ARMA ModelsInput Variables and Regression with ARMA ErrorsIntervention Models and Interrupted Time SeriesRational Transfer Functions and Distributed Lag ModelsForecasting with Input VariablesData Requirements -
Syntax
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DetailsThe Inverse Autocorrelation FunctionThe Partial Autocorrelation FunctionThe Cross-Correlation FunctionThe ESACF MethodThe MINIC MethodThe SCAN MethodStationarity TestsPrewhiteningIdentifying Transfer Function ModelsMissing Values and AutocorrelationsEstimation DetailsSpecifying Inputs and Transfer FunctionsInitial ValuesStationarity and InvertibilityNaming of Model ParametersMissing Values and Estimation and ForecastingForecasting DetailsForecasting Log Transformed DataSpecifying Series PeriodicityDetecting OutliersOUT= Data SetOUTCOV= Data SetOUTEST= Data SetOUTMODEL= SAS Data SetOUTSTAT= Data SetPrinted OutputODS Table NamesStatistical Graphics
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Examples
- References
- The Three Stages of ARIMA Modeling
- Identification Stage
- Estimation and Diagnostic Checking Stage
- Forecasting Stage
- Using ARIMA Procedure Statements
- General Notation for ARIMA Models
- Stationarity
- Differencing
- Subset, Seasonal, and Factored ARMA Models
- Input Variables and Regression with ARMA Errors
- Intervention Models and Interrupted Time Series
- Rational Transfer Functions and Distributed Lag Models
- Forecasting with Input Variables
- Data Requirements
This section outlines the use of the ARIMA procedure and gives a cursory description of the ARIMA modeling process for readers who are less familiar with these methods.