Interrupted ARIMA

Fits seasonal and non-seasonal ARIMA (p, d, q)(pS, dS, qS) models to continuous variables, and estimates parameters for one or more interventions (discontinuities) of different types; various results graphs, forecasts, and tools for assessing the quality fit to the data are reported by the program.

Element Name Description
ARIMA Model
Autoregressive parameters p Number of nonseasonal autoregressive parameters p in the ARIMA (p, d, q)(pS, dS, qS) model.
Moving average parameters q Number of nonseasonal moving average parameters q in the ARIMA (p, d, q)(pS, dS, qS) model.
Seasonal autoregressive pS Number of seasonal autoregressive parameters pS in the ARIMA (p, d, q)(pS, dS, qS) model.
Seasonal moving average qS Number of seasonal moving average parameters qS in the ARIMA (p, d, q)(pS, dS, qS) model.
Seasonal lag Specifies the seasonal lag that is applied to the seasonal autoregressive and/or moving average parameters (pS, qS).
Estimate ARIMA constant Includes a Constant parameter in the model.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.
Interventions (1-3)
Estimate intervention 1 Estimates (evaluates) the parameters for intervention 1.
Intervention 1 at case number Specifies the first case number where the intervention occurred.
Intervention 1 type Specifies the type of intervention; McDowall et al. (1980) distinguish among three major types of impacts that are possible: (1) Abrupt, permanent, (2) Gradual, permanent, and (3) Abrupt, temporary; refer to the Electronic Manual for details.
Estimate intervention 2 Estimate (evaluate) the parameters for intervention 2.
Intervention 2 at case number Specifies the second case number where the intervention occurred.
Intervention 2 type Specifies the type of intervention; McDowall et al. (1980) distinguish among three major types of impacts that are possible: (1) Abrupt, permanent, (2) Gradual, permanent, and (3) Abrupt, temporary; refer to the Electronic Manual for details.
Estimate intervention 3 Estimate (evaluate) the parameters for intervention 3.
Intervention 3 at case number Specifies the third case number where the intervention occurred.
Intervention 3 type Specifies the type of intervention; McDowall et al. (1980) distinguish among three major types of impacts that are possible: (1) Abrupt, permanent, (2) Gradual, permanent, and (3) Abrupt, temporary; refer to the Electronic Manual for details.
Intervention (4-6)
Estimate intervention 4 Estimate (evaluate) the parameters for intervention 4.
Intervention 4 at case number Specifies the fourth case number where the intervention occurred.
Intervention 4 type Specifies the type of intervention; McDowall et al. (1980) distinguish among three major types of impacts that are possible: (1) Abrupt, permanent, (2) Gradual, permanent, and (3) Abrupt, temporary; refer to the Electronic Manual for details.
Estimate intervention 5 Estimate (evaluate) the parameters for intervention 5.
Intervention 5 at case number Specifies the fifth case number where the intervention occurred.
Intervention 5 type Specifies the type of intervention; McDowall et al. (1980) distinguish among three major types of impacts that are possible: (1) Abrupt, permanent, (2) Gradual, permanent, and (3) Abrupt, temporary; refer to the Electronic Manual for details.
Estimate intervention 6 Estimate (evaluate) the parameters for intervention 6.
Intervention 6 at case number Specifies the sixth case number where the intervention occurred.
Intervention 6 type Specifies the type of intervention; McDowall et al. (1980) distinguish among three major types of impacts that are possible: (1) Abrupt, permanent, (2) Gradual, permanent, and (3) Abrupt, temporary; refer to the Electronic Manual for details.
Transformations
Difference series When the Difference is selected, the series is differenced. Non-seasonal (1) and seasonal (2) differencing can be requested. Specify the respective Lag and the number of difference passes that are to be performed.
Difference series, lag 1 When the Difference is selected, the series is differenced. Non-seasonal (1) and seasonal (2) differencing can be requested. Specify the respective Lag and the number of difference passes that are to be performed.
Passes, differencing 1 When the Difference is selected, the series is differenced. Non-seasonal (1) and seasonal (2) differencing can be requested. Specify the respective Lag and the number of difference passes that are to be performed.
Difference series, lag 2 When the Difference is selected, the series is differenced. Non-seasonal (1) and seasonal (2) differencing can be requested. Specify the respective Lag and the number of difference passes that are to be performed.
Passes, differencing 2 When the Difference is selected, the series is differenced. Non-seasonal (1) and seasonal (2) differencing can be requested. Specify the respective Lag and the number of difference passes that are to be performed.
Power-transformation Select the Power transformation to raise each value in the series to the power of constant C, where C is the value specified as the power transform parameter. The transformations are performed prior to the analyses, e.g., to stabilize the variance over the series, etc.
Power-transform parameter When the Power-transformation is selected, each value in the series is raised to the power of C, where C is the value specified in this box.
Natural-log transformation Select the Natural-log transformation to compute the natural log for each value. The transformations are performed prior to the analyses, e.g., to stabilize the variance over the series, etc.
Estimation
Estimation method Select the procedure for estimating the ARIMA model parameters. In general, the estimation procedure will maximize the likelihood of the data, given the respective model.
Number of backcasts Applicable to the Approximate McLeod and Sales estimation method only; specify a number greater than 0 to request the backcast-cases estimation method; this method may be somewhat slower than the Approximate maximum likelihood method without backcasts; however, usually when the N of the series is relatively small and/or some parameter values are close to 1.0 (or -1.0) the estimates derived in this manner tend to be closer to the equivalent Exact maximum likelihood parameters.
Max n iterations, backcasting Specifies the maximum number of iterations for backcasting; not applicable if the Number of backcasts is equal to 0, or if Exact maximum likelihood (ML) estimation is requested.
Max number of iterations Specifies the maximum number of iterations for the iterative parameter estimation procedure.
Convergence criterion Specifies the Convergence criterion (required accuracy) for the iterative parameter estimation procedure; the estimation procedure will terminate when the changes in the ARIMA parameters over consecutive iterations are less than this value.
Results
Detail of computed results reported Specifies the detail of computed results reported. If Comprehensive results is requested, the autocorrelation function for the residuals and various additional residual plots are reported; if All results is requested, the original and transformed (for the analysis) series is also displayed and plotted.
Labels plots with case names Labels the horizontal axis in plots with case numbers or case names (if available).
p; for highlighting Specifies the p value used for highlighting significant results (e.g., parameter estimates) in spreadsheets.
White noise standard errors Creates the standard errors for the autocorrelation function for the ARIMA residuals under the assumption that the series is a white noise process.
Number of lags Specifies the maximum number of lags for evaluating the autocorrelation function for the ARIMA residuals.
Forecasting
Creates ARIMA forecasts Creates and display ARIMA forecasts.
Number Of cases to forecast Number of cases to forecast in the plot of the fitted ARIMA model.
p; for confidence limits Specifies p to compute confidence limits for the ARIMA forecasts.
Missing Data
Replace missing data Specifies how missing data is to be replaced. Missing data can be replaced by the overall mean, interpolated from adjacent points, replaced by the mean or median of N adjacent points (on both sides of the hole), or estimated (predicted) from linear trend regression.

 Note that as long as the missing data are at the end of the series (trailing missing data) or the beginning of the series (leading missing data), the missing data will simply be ignored.
Number of adjacent points Applicable if missing data are replaced by the mean or median of N adjacent points; specify N.

 The missing data are replaced by the mean or median computed from the N adjacent points on both sides of the hole of missing data.