Introduction to Time Series Analysis Using IBM SPSS Modeler (v18.1.1) Training in Switzerland

• Familiarity with the IBM SPSS Modeler environment (creating, editing, opening, and saving streams).
• General knowledge of regression analysis is recommended but not required

Introduction to time series analysis

Automatic forecasting with the Expert Modeler

Measuring model performance

Time series regression

Exponential smoothing models

ARIMA modeling

1: Introduction to time series analysis
• Explain what a time series analysis is
• Describe how time series models work
• Demonstrate the main principles behind a time series forecasting model
2: Automatic forecasting with the Expert Modeler
• Examine fit and error
• Examine unexplained variation
• Examine how the Expert Modeler chooses the best fitting time series model
3: Measuring model performance
• Discuss various ways to evaluate model performance
• Evaluate model performance of an ARIMA model
• Test a model using a holdout sample
4: Time series regression
• Use regression to fit a model with trend, seasonality and predictors
• Handling predictors in time series analysis
• Detect and adjust the model for autocorrelation
• Use a regression model to forecast future values
5: Exponential smoothing models
• Types of exponential smoothing models
• Create a custom exponential smoothing model
• Forecast future values with exponential smoothing
• Validate an exponential smoothing model with future data
6: ARIMA modeling
• Explain what ARIMA is
• Learn how to identify ARIMA model types
• Use sequence charts and autocorrelation plots to manually identify an ARIMA model that fits the data
• Check your results with the Expert Modeler