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

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