Time series data are intriguing yet complicated information to work with. While this course will provide students with a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is a small first step in being able to confidently understand what trends might exist within a set of data and the complexities of being able to use this information to make predictions or forecasts. Yet, whether it is financial, medical or weather related, this type of data is quite frequently found in much of our daily lives.
Topics typically covered in this graduate level course include:
- Understanding the characteristics of time series data
- Understanding moving average models and partial autocorrelation as foundations for analysis of time series data
- Exploratory Data Analysis - Trends in time series data
- Using smoothing and removing trends when working with time series data
- Understanding how periodograms are used with time series data
- Implementing ARMA and ARIMA time series models
- Identifying and interpreting various patterns for intervention effects
- Examining the analysis of repeated measures design
- Using ARCH and AR models in multivariate time series contexts
- Using spectral density estimation and spectral analysis
- Using fractional differencing and threshold models with time series data
Dr. Megan Romer is the current author of the materials used in this course. The material builds on that of the course's previous authors, Robert Heckard and John Fricks.
This course makes extensive use of the R Statistical Software. This is open-source free software that can be downloaded from the R Project home page. For more information and links to download this software please see the Statistical Software page. MS Word is also required.
R involves programming. Students should be a quick learner of software packages. Students who have no experience with programming or are anxious about being able to manipulate software code are strongly encouraged to take the one-credit course in R in order to establish this foundation.
R will be supported and sample programs will be supplied but you will be required to do some programing on your own. Due to different software applications, software versions, and platforms, there may be issues with running code. Students must be proactive in seeking advice and help from appropriate sources, including documentation resources, the class discussion forum, the teaching assistant, instructor or helpdesk.
Shumway R.H., Stoffer, D.S. (2012). Time Series Analysis and Its Applications With R Examples, 4th Edition, Springer. ISBN: 978-3319524511
(The text is required, though students do not have to purchase it because it is available electronically through the Penn State library.)
Lab / Homework Activities - will be given weekly. In order to receive credit for homework, all assignments must include HOW an answer is obtained, not just the numerical solution. These assignments can be compiled in Word, however, submission as a .pdf is prefered.
Exams - There will be one mid-term and one final exam. These are 'take-home' application oriented exams that should be completed in the time specified by the instructor.
STAT 462 - Applied Regression Analysis, or
STAT 501 - Regression Methods, or
STAT 511 - Regression Analysis and Modeling