Time Series Analysis


This course presents both theory and applications of linear time series and an introduction to stochastic processes at a level accessible to a wide variety of students and practitioners in statistics, economics and finance, science, engineering and quantitative social science. Stochastic processes quantify dynamic relationships of sequences of random events; time series modelling is one part of stochastic processes. Emphasis is placed on model choice and development, and how to estimate model parameters and forecast future values. Stochastic models play an important role in analysing the variability inherent in biological, medical and engineering processes, and in dealing with the uncertainties affecting managerial decisions and the complexities of psychological and social interactions.



  • Semester 2 - 2016

Learning Outcomes

1. Understand the concepts of time series analysis in the time domain;

2. Be able to determine and apply appropriate models for the real life datasets;

3. Have developed skills in statistical computing of time series problems.

4. Understand the concepts of stochastic processes;

5. Be able to determine and apply Markov chain and Markov processes into real life phenomenon..


  • Introduction and Review
  • Fundamental concepts in Time Series (TS)
  • Model Stationary TS
  • Model Nonstationary TS
  • Model specification TS
  • Model estimation TS
  • Model diagnostics TS
  • Model forecasting TS
  • Introduction to Stochastic Processes
  • Markov Chains
  • Markov Processes

Assumed Knowledge

STAT2010 - Fundamentals of Statistics

Assessment Items

Written Assignment: Written assignment 1

Project: Project

Written Assignment: Assignment 2

Formal Examination: Examination ¿ Formal

Contact Hours


Computer Lab

Face to Face On Campus 2 hour(s) per Week for Full Term


Face to Face On Campus 2 hour(s) per Week for Full Term