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Introduction to Time Series Analysis Using IBM SPSS Modeler (v18.1.1) (0A028G)

Detailed Course Outline

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