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Curricular information is subject to change
Upon completion of this module students will be able to:
1. Identify the stationarity properties of a time series,
2. Model the time series using Box-Jenkins ARIMA techniques,
3. Estimate parameters for ARIMA models using a variety of procedures,
4. Produce forecasts for a given time series,
5. Be familiar with additional topics such as cointegration, vector auto regressive models..
|Student Effort Type||Hours|
|Specified Learning Activities||
|Autonomous Student Learning||
Familiarity with basic probability concepts such as Probability distribution, Expectation, Variance, Covariance and Correlation. Knowledge of the main probability distributions (normal distribution, chi-square, ...). Basic linear algebra (vectors, matrices).Learning Recommendations:
Students should have a knowledge of statistical inference at a level equivalent to that which would be achieved upon completion of either "Inferential Statistics" STAT20100 or "Inference for Data Analytics" STAT30280.
Basic knowledge of linear algebra (vectors, matrices). Knowledge of linear models and least square estimation which would be achieved upon completion of "Data Modelling for Science" STAT20070 or "Predictive Analytics I" STAT30240 or STAT40790 would be beneficial.
|Description||Timing||Component Scale||% of Final Grade|
|Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets.||Throughout the Trimester||n/a||Graded||No||
|Examination: 2 hour end of semester examination||2 hour End of Trimester Exam||No||Graded||No||
|Resit In||Terminal Exam|
|Spring||Yes - 2 Hour|
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
|Mr John O'Sullivan||Tutor|