When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
This IBM Self-Paced Virtual Class (SPVC) includes: - PDF course guide available to attendee during and after course - Lab environment where students can work through demonstrations and exercises at...Read More
This IBM Self-Paced Virtual Class (SPVC) includes:
- PDF course guide available to attendee during and after course
- Lab environment where students can work through demonstrations and exercises at their own pace
Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.
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. /terms
Please refer to course overview
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
- Familiarity with the IBM SPSS Modeler environment (creating, editing, opening, and saving streams). - General knowledge of regression analysis is recommended but not required
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 model2: Automatic forecasting with the Expert Modeler- Examine fit and error- Examine unexplained variation- Examine how the Expert Modeler chooses the best fitting time series model3: Measuring model performance- Discuss various ways to evaluate model performance- Evaluate model performance of an ARIMA model- Test a model using a holdout sample4: 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 values5: 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 data6: 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