SPSS Forecasting enables analysts to predict trends and develop forecasts quickly and easily—without being an expert statistician. People new to forecasting can create sophisticated forecasts that take into account multiple variables, and experienced forecasters can use SPSS Forecasting to validate their models. Examples of time series forecasting include predicting the number of staff required each day for a call center, or forecasting the demand for a particular product or service. SPSS Forecasting helps you every step of the way, so you get the information you need faster.

If you’re new to building models from time-series data, SPSS Forcasting helps you by:

  • Generating reliable models, even if you’re not sure how to choose exponential smoothing parameters or ARIMA orders, or how to achieve stationarity. 

  • Automatically testing your data for seasonality, intermittency, and missing values, and selecting appropriate models n Detecting outliers and preventing them from influencing parameter estimates.

  • Generating graphs showing confidence intervals and the model’s goodness of fit.

  • Control every parameter when building your data model.

  • Or use SPSS Trends’ Expert Modeler recommendations as a starting point or to check your work.



Model a set of time-series variables by using the Expert Modeler or by specifying the structure of autoregressive integrated moving average (ARIMA) or exponential smoothing models.

  • Allow Expert Modeler to select the best fitting
    predictor variables and models

    • Limit search space to ARIMA models
      only, or to exponential smoothing
      models only.

    • Treat independent variables as events.

  • Specify custom ARIMA models, which produce maximum likelihood estimates for seasonal and non-seasonal univariate models.

    • Work with general or constrained models
      specified by autoregressive or moving
      average order, order of differencing, seasonal autoregressive or moving average order, and seasonal differencing.

    • Use two dependent variable transformations: square root and natural log.

    • Automatically detect or specify outliers:
      additive, level shift, innovational, transient, seasonal additive, local trend, and additive patch.

    • Specify seasonal and non- seasonal numerator, denominator, and difference transfer function orders and transformations for each independent variable.

  • Specify custom exponential smoothing models.

    • Four non-seasonal model types: simple,
      Holt’s linear trend, Brown’s linear trend,
      and damped trend.

    • Three seasonal model types: simple
      seasonal, Winters’ additive, and Winters’

    • Two dependent variable transformations:
      Square root and natural log.

  • Display forecasts, fit measures, Ljung-Box
    statistic, parameter estimates, and outliers
    by model.

  • Generate tables and plots to compare
    statistics across all models.

  • Choose from eight available goodness-offit
    measures: R2, stationary R2, root mean
    square error, mean absolute percentage
    error, mean absolute error, maximum
    absolute percentage error, maximum
    absolute error, and normalized Bayes
    information criterion (BIC).

  • Create tables and plots of residual autocorrelation function (ACF) and partial autocorrelation function (PACF).

  • Plot observed values, forecasts, fit values,
    and confidence intervals for forecasts, and fit values for each series.

  • Filter output to a fixed number or percentage of best- or worst-fitting models n Save predicted values, lower confidence limits, upper confidence limits, and noise residuals for each series back to the dataset n Specify forecast period, treatment of user-missing values, and confidence intervals n Export models to an XML file for later use by TSAPPLY.

Apply saved models to new or updated data


  • Simultaneously apply models from multiple XML files created with TSMODEL.

  • Re-estimate model parameters and goodness-of-fit measures from the data, or load them from the saved model file.

  • Selectively choose saved models to apply n Override the periodicity (seasonality) of the active dataset.

  • Choose from the same output, fit measure, statistics, and options as TSMODEL.

  • Export re-estimated models to an XML file.

Estimate multiplicative or additive seasonal

factors for periodic time series.

  • Choose either a multiplicative or an additive model.

  • Calculate moving averages, ratios, seasonal and seasonal adjustment factors, seasonally adjusted series, smoothed trend-cycle components, and irregular components.

Decompose a time series into its harmonic

components, a set of regular periodic functions at different wavelengths or periods.

  • Produce/plot univariate or bivariate periodogram and spectral density estimates.

  • Produce/plot bivariate spectral analyses n Smooth periodogram values with weighted moving averages.

  • Smooth, using available spectral data windows: Tukey-Hamming, Tukey, Parzen, Bartlett, equal weight, no smoothing, and user-specified weights.

  • Produce high-resolution charts: 
    Periodogram, spectral and cospectral density estimate, squared coherency, quadrature spectrum estimate, phase spectrum, cross amplitude, and gain.


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