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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS BIL 3108 TIME SERIES MINING ELECTIVE 3 0 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR ESIN FIRUZAN

Offered to

Computer Science

Course Objective

This course is aimed to use data mining techniques to analyze time series.

Learning Outcomes of the Course Unit

1   Have a good understanding of the general scope of the time series. 2   Have a good understanding of data mining techniques for the problems of time series. 3   Have a good understanding of the computer programming language to modeling of time series. 4   Have a good understanding of mathematical solution methods. 5   Have a good ability to understand the theory of the time series analysis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Temporal data types and preprocessing 2 Time Series Similarity measures 3 Time Series representation 4 Time Series representation (Continues to ) 5 Time Series representation (Continues to ) (Quiz 1) 6 Time Series summarization methods 7 Temporal event representation 8 Midterm exam 9 Temporal data classification 10 Temporal data classification (Continues to ) 11 Temporal data clustering (Quiz 2) 12 Temporal data clustering (Continues to ) 13 Outlier analysis 14 General review

Recomended or Required Reading

Textbook(s): M. Last, A. Kandel, H. Bunke, Data Mining In Time Series Databases, World Scientific Pub Co Inc., 2004. Supplementary Book(s): T. Mitsa, Temporal Data Mining, Chapman and Hall, 2010.

Planned Learning Activities and Teaching Methods

Lecture format, built around the textbook readings with numerous examples chosen to illustrate theoretical concepts.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 QUZ QUIZ 3 ASG ASSIGNMENT 4 PAR PARTICIPATION 5 FIN FINAL EXAM 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.35 + QUZ * 0.05 + ASG * 0.05 + PAR * 0.05 + FIN * 0.50 7 RST RESIT 8 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.35 + QUZ * 0.05 + ASG * 0.05 + PAR * 0.05 + RST * 0.50 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

cagin.kandemir@deu.edu.tr ovgu.tekin@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 3 39 Preparation before/after weekly lectures 12 3 36 Preparation for Mid-term Exam 1 10 10 Preparation for Final Exam 1 30 30 Preparation for Quiz etc 2 1 2 Preparing Individual Assignments 4 2 8 Final 1 2 2 Mid-term 1 2 2 Quiz etc. 2 0,5 1 TOTAL WORKLOAD (hours) 130

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 LO.1 5 4 5 5 4 LO.2 5 4 5 5 3 LO.3 5 4 5 5 5 4 LO.4 5 5 5 5 4 4 5 LO.5 5 5 5 5