Time Series Analysis

Description

Time series analysis is a statistical methodology to exploit historical data generated by real world systems to forecast the future of these systems. This course presents both theory and applications of time series analysis at a level accessible to a wide variety of students and practitioners in statistics, economics and finance, science, engineering and quantitative social sciences. Emphasis is placed on the development and choice of appropriate models, how to estimate and test model parameters and forecast future values.

Availability

Callaghan

  • Semester 2 - 2017

Learning Outcomes

On successful completion of the course students will be able to:

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.

Content

  • 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

Assumed Knowledge

STAT2010 - Fundamentals of Statistics

Assessment Items

Written Assignment: Written assignments

Project: Project

Formal Examination: Final Examination

Contact Hours

Callaghan

Computer Lab

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

Lecture

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