You can use the OUTFOR= option in the FORECAST statement to store the series and component forecasts produced by the procedure. This data set contains the following columns:
the BY variables
the ID variable. If an ID variable is not specified, then a numerical variable, _ID_, is created that contains the observation numbers from the input data set.
the dependent series and the predictor series
FORECAST, a numerical variable containing the one-step-ahead predicted values and the multistep forecasts
RESIDUAL, a numerical variable containing the difference between the actual and forecast values
STD, a numerical variable containing the standard error of prediction
LCL and UCL, numerical variables containing the lower and upper forecast confidence limits
S_SERIES and VS_SERIES, numerical variables containing the smoothed values of the dependent series and their variances
S_IRREG and VS_IRREG, numerical variables containing the smoothed values of the irregular component and their variances. These variables are present only if the model has an irregular component.
F_LEVEL, VF_LEVEL, S_LEVEL, and VS_LEVEL, numerical variables containing the filtered and smoothed values of the level component and the respective variances. These variables are present only if the model has a level component.
F_SLOPE, VF_SLOPE, S_SLOPE, and VS_SLOPE, numerical variables containing the filtered and smoothed values of the slope component and the respective variances. These variables are present only if the model has a slope component.
F_AUTOREG, VF_AUTOREG, S_AUTOREG, and VS_AUTOREG, numerical variables containing the filtered and smoothed values of the autoreg component and the respective variances. These variables are present only if the model has an autoreg component.
F_CYCLE, VF_CYCLE, S_CYCLE, and VS_CYCLE, numerical variables containing the filtered and smoothed values of the cycle component and the respective variances. If there are multiple cycles in the model, these variables are sequentially numbered as F_CYCLE1, F_CYCLE2, etc. These variables are present only if the model has at least one cycle component.
F_SEASON, VF_SEASON, S_SEASON, and VS_SEASON, numerical variables containing the filtered and smoothed values of the season component and the respective variances. If there are multiple seasons in the model, these variables are sequentially numbered as F_SEASON1, F_SEASON2, etc. These variables are present only if the model has at least one season component.
F_BLKSEAS, VF_BLKSEAS, S_BLKSEAS, and VS_BLKSEAS, numerical variables containing the filtered and smoothed values of the blockseason component and the respective variances. If there are multiple block seasons in the model, these variables are sequentially numbered as F_BLKSEAS1, F_BLKSEAS2, etc.
F_SPLSEAS, VF_SPLSEAS, S_SPLSEAS, and VS_SPLSEAS, numerical variables containing the filtered and smoothed values of the splineseason component and the respective variances. If there are multiple spline seasons in the model, these variables are sequentially numbered as F_SPLSEAS1, F_SPLSEAS2, etc. These variables are present only if the model has at least one splineseason component.
Filtered and smoothed estimates, and their variances, of the time-varying regression coefficients of the variables specified in the RANDOMREG and SPLINEREG statements. A variable is not included if its coefficient is time-invariant, that is, if the associated disturbance variance is zero.
S_TREG and VS_TREG, numerical variables containing the smoothed values of level plus regression component and their variances. These variables are present only if the model has at least one predictor variable or has dependent lags.
S_TREGCYC and VS_TREGCYC, numerical variables containing the smoothed values of level plus regression plus cycle component and their variances. These variables are present only if the model has at least one cycle or an autoreg component.
S_NOIRREG and VS_NOIRREG, numerical variables containing the smoothed values of the sum of all components except the irregular component and their variances. These variables are present only if the model has at least one seasonal or block seasonal component.