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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) $868.95
International Tuition Fees (NZD) $3,656.70

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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.
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.

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Second Semester

Teaching method
This paper is taught On Campus
Learning management system


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


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