Introduction to Applied Time Series Analysis & Forecasting
Date/Time: November 12th 14:00 to 17:00
Location: SJSU College of Social Sciences - WSQ 113
Instructor: Dr. Egbe-Etu Etu
This workshop is sponsored by the generous funding from Public Interest Technology University Network grant to provide support for educational activities focused on equitable access to technologies.
Dr. Egbe-Etu Etu is an Assistant Professor of Business Analytics at San Jose State University (SJSU). Before joining SJSU, Dr. Etu received his Ph.D. in Industrial and Systems Engineering from Wayne State University in 2021 and his bachelor’s degree in Civil Engineering from Covenant University, Nigeria, in 2016. His research interest centers on the development of use-inspired machine learning models to solve challenging business problems in healthcare, manufacturing, and transportation. The main aim of his research is to develop decision-support tools that will help business professionals do their best work, improve resilience and overall system performance while minimizing errors. He is a member of the Industrial Engineering and Operations Management (IEOM), Institute of Industrial & Systems Engineering (IISE), and SAVE International. In 2020, he received the IEOM Annual Conference Best Paper Award in the Healthcare Systems track.
Workshop description:
The objective of the course is to introduce you to time series analysis and forecasting. The orientation of the course is theoretical and applied. A theoretical understanding of time series and the forecasting problem is critical for understanding forecasting methods and using them effectively. Students will learn several important tools to provide trend analytics and forecasting based on past data and time series. Students will be able then to apply the tools and techniques of time series analysis to complex problems in areas such as public health, travel demand forecasting and climate forecasting to reach effective solutions.
Learning outcomes:
- Learn how to construct a time-series plot & identify the underlying patterns in the data.
- Learn basic concepts in time series regression.
- Learn how to develop forecasts for a time series that has a seasonal pattern.
- Utilize R for computation, visualization, and analysis of time series data.
Class outline
- Introduction to time series
- Time series patterns
- Forecast accuracy
- Time series regression
- Advanced time series modeling
- Applied project (class exercise)
Technology Requirements
We will utilize the R programming software for statistical analysis in this course.
Click here to download RStudio and R for windows. Also see RStudio and R for mac. The following YouTube tutorials will help you install the files:
Windows – RStudio and R·
Mac – RStudio and R
Fees
Individual course fee:
Early bird registration (until Oct. 20, 2022): $275
Regular registration (From Oct. 21, 2022): $300
Full program fee (including all of the workshops):
Early bird registration (Until Oct. 20, 2022): $975
Regular registration (From Oct. 21, 2022): $1000
