Overview: HPFDIAGNOSE Procedure

The HPFDIAGNOSE procedure provides a comprehensive set of tools for automated univariate time series model identification. Time series data can have outliers, structural changes, and calendar effects. In the past, finding a good model for time series data usually required experience and expertise in time series analysis.

The HPFDIAGNOSE procedure automatically diagnoses the statistical characteristics of time series and identifies appropriate models. The models that HPFDIAGNOSE considers for each time series include autoregressive integrated moving average with exogenous inputs (ARIMAX), exponential smoothing, and unobserved components models. Log transformation and stationarity tests are automatically performed. The ARIMAX model diagnostics find the autoregressive (AR) and moving average (MA) orders, detect outliers, and select the best input variables. The unobserved components model (UCM) diagnostics find the best components and select the best input variables.

The HPFDIAGNOSE procedure provides the following functionality:

• intermittency (or interrupted series) test

• functional transformation test

• simple differencing and seasonal differencing tests

• tentative simple ARMA order identification

• tentative seasonal ARMA order identification

• outlier detection

• significance test of events (indicator variables)

• transfer function identification

  • intermittency test

  • functional transformation for each regressor

  • simple differencing order and seasonal differencing order for each regressor

  • time delay for each regressor

  • simple numerator and denominator polynomial orders for each regressor

• intermittent demand model (automatic selection)

• exponential smoothing model (automatic selection)

• unobserved components model (automatic selection)

PROC HPFDIAGNOSE can be abbreviated as PROC HPFDIAG.