Introduction to Time Series Analysis Using IBM SPSS Modeler (v18.1.1) SPVC - 0E028G

Description

This course gets you up and running with a set of procedures for analyzing time series data. Learn how to forecast using a variety of models, including regression, exponential smoothing, and ARIMA, which take into account different combinations of trend and seasonality. The Expert Modeler features will be covered, which is designed to automatically select the best fitting exponential smoothing or ARIMA model, but you will also learn how to specify your own custom models, and also how to identify ARIMA models yourself using a variety of diagnostic tools such as time plots and autocorrelation plots.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

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

Aim

Please refer to course overview

Participant requirements

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

Participant

Roles: Business Analyst, Data Scientist
Specifically, this is an introductory course for:
Anyone who is interested in getting up to speed quickly and efficiently using the IBM SPSS Modeler forecasting capabilities