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

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

Objective

Introduction to time series analysis

Automatic forecasting with the Expert Modeler

Measuring model performance

Time series regression

Exponential smoothing models

ARIMA modeling