STATS 786 Forecasting for Data Science
Forecasting for Data Science
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STATS 326: Applied Time Series
Analysis
STATS 786: Time Series
Forecasting for Data Science
Instructors
Lecture times (NZ time)
Tue 5–6 PM, Owen G Glenn, Room 051
Thu 5–6 PM, Clock Tower - South, Room 039
Tutorial times (NZ time)
Fri 12–1 PM, Science B302, Room G40
Fri 1–2 PM, Science B302, Room G40
At least for the first half of the semester, both lectures and
tutorials will be online.
We will be using tutorial times as office hours. Depending on how
many students turn up, we might increase or decrease the tutorial
hours over the semester.
Learning objectives
1 Use appropriate data visualizations to identify the features present
in time series.
2 Identify the most appropriate time series models for a given
problem.
3 Fit commonly used time series models using R.
4 Interpret and understand the software output for a given time series
model.
5 Perform model selection and cross-validation.
6 Develop computer skills required to forecast time series data.
4
Learning objectives
1 Use appropriate data visualizations to identify the features present
in time series.
2 Identify the most appropriate time series models for a given
problem.
3 Fit commonly used time series models using R.
4 Interpret and understand the software output for a given time series
model.
5 Perform model selection and cross-validation.
6 Develop computer skills required to forecast time series data.
4
Are you interested in learning time series theory...?
Take STATS 726!
References
5
Assessments
Assessment type Due date Percentage
Quizzes See course plan 5% (5× 1%)
Assignments See course plan 15% (3× 5%)
Mid-term test (1 hour) Tuesday 3 May 10%
Group project Friday 3 June 20%
Exam (2 hours) Official exam period 50%
The mid-term test starts at 5 PM (NZ time). Additional time will be given
to upload your solutions on Canvas.
Collaboration is encourage because it opens a space to discuss the
topics that you learn with classmates.
You have to write your own code or/and explanations, and must not
copy another person’s exercise/assignment answers.
6
Assessments
Assessment type Due date Percentage
Quizzes See course plan 5% (5× 1%)
Assignments See course plan 15% (3× 5%)
Mid-term test (1 hour) Tuesday 3 May 10%
Group project Friday 3 June 20%
Exam (2 hours) Official exam period 50%
The mid-term test starts at 5 PM (NZ time). Additional time will be given
to upload your solutions on Canvas.
Collaboration is encourage because it opens a space to discuss the
topics that you learn with classmates.
You have to write your own code or/and explanations, and must not
copy another person’s exercise/assignment answers.
6
Assessments
Assessment type Due date Percentage
Quizzes See course plan 5% (5× 1%)
Assignments See course plan 15% (3× 5%)
Mid-term test (1 hour) Tuesday 3 May 10%
Group project Friday 3 June 20%
Exam (2 hours) Official exam period 50%
The mid-term test starts at 5 PM (NZ time). Additional time will be given
to upload your solutions on Canvas.
Collaboration is encourage because it opens a space to discuss the
topics that you learn with classmates.
You have to write your own code or/and explanations, and must not
copy another person’s exercise/assignment answers. 6
Software and main packages