Time Series Analysis and Forecasting

Overview

In a world overflowing with data, the ability to analyze time series and forecast future trends is more valuable than ever. This course equips you with the essential tools to decode the patterns hidden within time-based data and make accurate predictions across diverse fields like economics, finance, healthcare, and more.

After the course, you should be able to

• Understand time series data and its properties
• Identify and explain common patterns in time series data
• Understand and apply autocorrelation
• Understand the concept of stationarity and its importance in forecasting
• Perform data adjustments and transformations
• Decompose a time series into its components
• Apply forecasting methods
• Perform residual diagnostics
• Calculate prediction intervals
• Evaluate forecasting performance using appropriate measures

Books

There are no required textbooks for this course. However, you may find the following list of books useful throughout the lecture:

• Brockwell, P. J., & Davis, R. A. (Eds.). (2002). Introduction to time series and forecasting. New York, NY: Springer New York.
• Hyndman, R. J. (2018). Forecasting: principles and practice. OTexts.
• Shumway, R. H., Stoffer, D. S., & Stoffer, D. S. (2000). Time series analysis and its applications (Vol. 3, p. 4). New York: springer.
• Mills, T. C. (2019). Applied time series analysis: A practical guide to modeling and forecasting. Academic press.

Grading

The following components will constitute your grade in this course:

• Assignments: 30% (each 10%)
• Presentation: 20%
• Mid Term exam: 20%
• Final exam: 30%