Accessibility Skip to Global Navigation Skip to Local Navigation Skip to Content Skip to Search Skip to Site Map Menu

STAT352 Applied Time Series

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
Paper code STAT352
Subject Statistics
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $851.85
International Tuition Fees (NZD) $3,585.00

^ Top of page

Prerequisite
STAT 241
Schedule C
Arts and Music, Science
Eligibility
Suitable for students doing a major or a minor in Statistics. It is also of interest to students in Economics or Finance.
Contact
twang@otago.ac.nz
Teaching staff
To be arranged
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
Textbooks are not required for this paper.

All learning materials, including lecture notes, example data sets and computer code, are provided online.
Course outline
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.

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
L1 Monday 13:00-13:50 28-34, 36-41
Wednesday 13:00-13:50 28-34, 36-41
AND
M1 Thursday 12:00-12:50 28, 30, 32, 34, 36, 38, 40

Tutorial

Stream Days Times Weeks
Attend
T1 Thursday 15:00-15:50 29-34, 36-40

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
Paper code STAT352
Subject Statistics
EFTS 0.1500
Points 18 points
Teaching period Second Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
STAT 241
Schedule C
Arts and Music, Science
Eligibility
Suitable for students doing a major or a minor in Statistics. It is also of interest to students in Economics or Finance.
Contact
twang@otago.ac.nz
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
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.

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

Lecture

Stream Days Times Weeks
Attend
L1 Monday 13:00-13:50 28-34, 36-41
Wednesday 13:00-13:50 28-34, 36-41
AND
M1 Thursday 12:00-12:50 28, 30, 32, 34, 36, 38, 40

Tutorial

Stream Days Times Weeks
Attend
T1 Thursday 15:00-15:50 29-34, 36-40