Training offering

Time Series Analysis and Forecasting with IBM SPSS Forecasting


Price £1,455 before tax
Length: 3 Days
Course code: 0G096G
Delivery Type Classroom

Session dates

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    On request. Please contact us.
This training is also available as onsite training. Feel free to contact us for more information.


This three-day course gets you up and running with a set of procedures for analyzing time series data. Learn how to forecast using a variety of models which take into account different combinations of trend, seasonality and prediction variables. The new Expert Modeler features in SPSS Trends 14.0 will be covered in this course. Generate predicted values along with standard errors, confidence intervals and residuals. This course will emphasize the graphical display of your results so you can visualize your forecasting models.


Please refer to course overview for description information.


This advanced course is for

  • SPSS users who are interested in getting up to speed quickly and efficiently using the SPSS forecasting capabilities.

  • Those who want to know the full capabilities of the Trends module and its Expert Modeler.


You should have:

  • On the job experience with SPSS for Windows or completion of the Basics and/or Intermediate SPSS for Windows courses.

  • No previous forecasting experience required.

  • For users of SPSS for Windows Base System, SPSS Trends.

  • It would be helpful to have a basic understanding of regression analysis.


  • The basics of forecasting

  • Smoothing time series data

  • Outliers and error in time series data

  • Automatic forecasting with the Expert Modeler

  • Assessing model performance

  • Fitting curves to time series data

  • Regression with time series data

  • Exponential smoothing models

  • ARIMA models

  • Applying a model to new data

  • Seasonal decomposition

  • Modeling seasonality

  • Intervention analysis

  • Transfer functions in ARIMA

  • Automatic forecasting of several time series


•    The Basics of Time Series Analysis
•    Starting Time Series Analysis
•    Smoothing time Series
•    Automatic Forecasting with Time Series Modeller
•    Measuring Model Performance
•    Exponential Smoothing Models
•    ARIMA models
•    Modelling Seasonality
•    Outliers Detection
•    Intervention Analysis
•    Transfer Functions

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