IBM SPSS Statistics

0G096G: Time Series Analysis and

Forecasting with IBM SPSS Forecasting


             Recommended Duration: 3 Days
Skill Level: Advanced
Cost: $3,300 (b/GST)

This three-day 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 which take into account different combinations of trend, seasonality and prediction variables. The new Expert Modeler features in SPSS Trends 14.0 will be covered in this course. Generate predicted values along with standard errors, confidence intervals and residuals. This course will emphasize the graphical display of your results so you can visualize your forecasting models.


Audience


This advanced course is for

  • SPSS users who are interested in getting up to speed quickly and efficiently using the SPSS forecasting capabilities.
  • Those who want to know the full capabilities of the Trends module and its Expert Modeler.

Prerequisites


Student should have:

  • On the job experience with SPSS for Windows or completion of the Basics and/or Intermediate SPSS for Windows courses.
  • No previous forecasting experience required.
  • For users of SPSS for Windows Base System, SPSS Trends.
  • It would be helpful to have a basic understanding of regression analysis.

Course Agenda


Summarize the components associated with each unit in the FlashSystem 840 Storage Implementation course
Differentiate between disk storage and flash storage
Recognize the evolution of FlashSystem products
Classify the FlashSystem 840 Management interface and functions
Recall host interfaces related to the FlashSystem 840
Summarize the integration of FlashSystem 840 with IBM SVC
Recall the administrative functions associated with the FlashSystem 840
Recognize the benefits of the Flashsystem V840


Course Overview


Day 1

  • The basics of forecasting
  • Smoothing time series data
  • Outliers and error in time series
  • Automatic forecasting with the Expert Modeler

Day 2

  • Assessing model performance
  • Fitting curves to time series data
  • Regression with time series data
  • Exponential smoothing models
  • ARIMA models

Day 3

  • Applying a model to new data
  • Seasonal decomposition
  • Modeling seasonality
  • Intervention analysis
  • Transfer functions in ARIMA
  • Automatic forecasting of several time series