An introduction to the practical aspects of the statistical analysis of time series and its application to the physical sciences and econometrics. Topics include seasonal decomposition, identification and estimation of ARIMA models, seasonal ARIMA models, and forecasting.
This paper examines a range of statistical techniques that can be used for the analysis of data that has been observed sequentially through time. Applications will be drawn from many disciplines ranging from econometrics to environmental monitoring.
|Paper title||Applied Time Series|
|Teaching period||Second Semester|
|Domestic Tuition Fees (NZD)||$868.95|
|International Tuition Fees (NZD)||$3,656.70|
- STAT 241
- Schedule C
- Arts and Music, Science
- Suitable for students doing a major or a minor in Statistics. It is also of interest to students in Economics or Finance.
- More information link
- View more information for STAT 352
- Teaching staff
- To be confirmed.
- Paper Structure
- Main topics:
- Classical decomposition of time series into a trend, seasonal and irregular component
- Models for stationary time series; autoregression, moving average and ARMA models; identification, estimation and diagnostic testing; forecasting from ARMA models
- ARIMA and seasonal ARIMA models for series with trend and seasonal components; forecasting
- Teaching Arrangements
- Five lectures per fortnight (alternating three and two weekly)
One tutorial per week
- Textbooks are not required for this paper.
All learning materials, including lecture notes, example data sets and computer code, are provided online.
- Course outline
- View course outline for STAT 352
- Graduate Attributes Emphasised
- Communication, Critical thinking, Self-motivation.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Students who successfully complete the paper will demonstrate the ability to critically approach real-world time series data analysis and appropriately handle the associated uncertainty.