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

Course Outline

1: Introduction to time series analysisExplain what a time series analysis isDescribe how time series models workDemonstrate the main principles behind a time series forecasting model2: Automatic forecasting with the Expert ModelerExamine fit and errorExamine unexplained variationExamine how the Expert Modeler chooses the best fitting time series model3: Measuring model performanceDiscuss various ways to evaluate model performanceEvaluate model performance of an ARIMA modelTest a model using a holdout sample4: Time series regressionUse regression to fit a model with trend, seasonality and predictorsHandling predictors in time series analysisDetect and adjust the model for autocorrelationUse a regression model to forecast future values5: Exponential smoothing modelsTypes of exponential smoothing modelsCreate a custom exponential smoothing modelForecast future values with exponential smoothingValidate an exponential smoothing model with future data6: ARIMA modelingExplain what ARIMA isLearn how to identify ARIMA model typesUse sequence charts and autocorrelation plots to manually identify an ARIMA model that fits the dataCheck your results with the Expert Modeler

Objective

Introduction to time series analysis

Automatic forecasting with the Expert Modeler

Measuring model performance

Time series regression

Exponential smoothing models

ARIMA modeling