Time Series Data and Analysis in SAS - Part 2
Thursdays, May 4 - May 25, 2023 from Noon - 1:00 PM ET
Course Description:
The SAS system has many powerful tools for modeling events that occur over time. This course offers an overview of SAS procedures and processes for time series data and analysis. The course is practical and example driven, emphasizing which procedures to consider and how to apply them in real situations. A quick introduction to each method is followed by two worked examples, with discussion of use cases, options in the SAS procedures, and producing graphical output. This four-part series focuses on time series data and basic analytic methods, including Regression, Lag Models, Time to Event, and forecasting using ARIMA and ARCH/GARCH. The strengths, weaknesses and optimal situations for each method are compared. Both Base SAS capabilities and SAS ETS procedures are described.
Course Outline:
Session 1 - May 4: State Space Models
Session 2 - May 11: Time Series Cluster Analysis
Session 3 - May 18: Time Series Plots and Data Visualization
Session 4: - May 25: Big Data Methods for Time Series
The SAS system has many powerful tools for modeling events that occur over time. This course offers an overview of SAS procedures and processes for time series data and analysis. The course is practical and example driven, emphasizing which procedures to consider and how to apply them in real situations. A quick introduction to each method is followed by two worked examples, with discussion of use cases, options in the SAS procedures, and producing graphical output. This four-part series focuses on time series data and basic analytic methods, including Regression, Lag Models, Time to Event, and forecasting using ARIMA and ARCH/GARCH. The strengths, weaknesses and optimal situations for each method are compared. Both Base SAS capabilities and SAS ETS procedures are described.
Course Outline:
Session 1 - May 4: State Space Models
- State Space Models
- Kalman Filter and Exponential Smoothing
- Unobserved Components Models
Session 2 - May 11: Time Series Cluster Analysis
- Time Series Clustering - Longitudinal Data
- Time Series Clustering - Seasonal Data
- Similarity Analysis
Session 3 - May 18: Time Series Plots and Data Visualization
- Heat Maps (Visual Design Choices)
- Violin Plot
- Geographic Times Series Plots
Session 4: - May 25: Big Data Methods for Time Series
- Data Processing Practices
- Sampling for Time Series Analysis
- Time Series Procedures Tuned for Big Data
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About the Instructor
Dr. David J Corliss is a statistical astrophysicist specializing in the dynamics of evolving stellar and cosmic populations. He has worked in the automotive industry for more than 20 years, with extensive work in dynamics of evolving populations of car buyers, reporting and visualization, operations research, big data methods, analytic platform design, and statistical methodology. Continuing astrophysics research part-time, an important focus of his work has been to bring new developments in academic research to industrial and private sector research. He presents regularly at local and national SAS events and other conferences, and is active as a leader in the statistics and data science community, writing a monthly column on Data for Good for Amstat News and serving as the non-academic rep for the Statistics section of the American Association for the Advancement of Science. David Corliss is the Founder and Director of Peace-Work, a volunteer cooperative of statisticians and data scientists providing analytic support for charitable groups and applying statistical methods to issue-driven advocacy in Data For Good projects.
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