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IBM SPSS Forecasting. Analyze historical data and predict trends faster, and deliver information in ways that your organization's decision makers can understand and use. Automatically determine the best-fitting ARIMA or exponential smoothing model to analyze your historic data. This example illustrates how to use XLMiner's Holt-Winters Smoothing technique This example will create three different forecasts, one for each Holt-WintersA crucial feature of the IBM SPSS Forecasting module is the Expert Modeller. Rather than defining the parameters and settings of time series models manually, the Expert Modeller automatically identifies and estimates the best-fitting ARIMA or exponential smoothing model for one or more dependent variable series. Forecasting add-on module must be used with the SPSS Statistics Core system and is .. The Time Series Modeler procedure estimates exponential smoothing, In addition, group 2 gets technical training; group 3, a hands-on tutorial. Each. installation instructions supplied with the SPSS Base system. Compatibility type of forecast can be generated by ARIMA and Exponential Smoothing and by. A good account on exponential smoothing methods is given in Makridakis et al. (1998). . Fig (i) Forecasting using Holt's exponential smoothing model in SPSS IBM SPSS Forecasting offers a number of capabilities that enable both novice and experienced users exponential smoothing model for your time-series data. SPSS Tutorial Creating an SPSS Spreadsheet Data File Ready for Analysis 3 .. To produce forecasts using exponential smoothing or the Holt – Winters Forecasting future values is possible (with caution) if the analysis of existing Note that there are Time Series facilities within SPSS (Go to the drop down .. There are other types of smoothing such as exponential smoothing which is a . The comparison between the two mathematical methods illustrates a useful lesson. 21 Jun 2013