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Meeting Information Instructor: Keshav P. Pokhrel, Ph.D. Meeting Times: MW 3:30 PM - 4:45PM Email: kpokhrel(at)umich.edu Meeting Location: 2046CB Fi Office: 2087CB Office Hours: Monday 10:30 AM- 12:00PM Wednesday 5:00 PM- 6:00PM Friday 10:30 AM- 12:00PM and by appointments Course Description and Objectives Description: This course covers topics in time series analysis and statistical techniques for forecasting. These are time series regression, decomposition methods, exponential smoothing, and the Box-Jenkins forecasting methodology. Objectives: The principle objective of the course is to introduce graduate and advanced undergraduate students in mathematics, economics, business, engineering, and any other field where the analysis of time series is important, to some of the many approaches to analyzing time series data.

In addition we will equip them with the tools and knowledge to make forecasts obtained from the statistical analysis of historical data. Student Leanrning Outcome: At the end of the course, the student will be able to • analyze time series data using various statistical approaches • generate reasonable forecast values Textbook Forecasting, Time Series, And Regression, 4th Edition, Bowerman, O'Connell, Koehler; ISBN-13: 9777, Brooks/ Cole. We will be covering chapters 1, 2, 3, 3, 4, 5, 6, 7, 8, 9, and 11*.

Apart from text book we will use different resources for the classroom activities and homeworks. Major Reference Books • R.

Hyndman and George Athanasopoulus • Markidakis, Wheelwright, and Hyndman • John E. Hanke, Dean Wichern • Diez, Barr, and Cetinkaya-Rundel, Homework At least five sets of homework problems will be assigned. Some addition homework problems will periodically be assigned during the lecture. Lowest homework grade will be dropped. For better exam results you need to master all the homework problems. Exams There will be two mid-term exams, and a final. To answer the exam questions, you are expected to have a clear mathematical reasoning of the statistical methods used to solve the subject problems.

Project There will be two mini-projects during the semester. For a good project, you need to describe the data, pose reasonable hypotheses, select appropriate time series model/s, compute the test results, and explain the results in both statistical terms and in plain English. Primary objective of these projects is to apply statistical methods in the real life situations.

Software We use a software called 'R'. R is a programming language for statistical computing and visualizing data. It can be downloaded for free from We will R Studio for regular classroom activities. Related searchesipadian 2 free download. R studio is an open source Integrated development Environment(IDE) for R.

To download R click. After Installing R: click to download R studio.

Evaluations and Important Dates: Exam I (20%) Monday, February 15 Exam II (20%) Monday, March 29 Mini Project I(10%) Due, February 24 Mini Project II(10%) Due, April 06 5 Homeworks (15%) TBD Final Exam(25%) Wednesday, April 27 (3:00PM-6:00PM) Grade Distribution Your final grade will be based on two mid-term exams, five sets of graded homeworks, two mini-projects, and a final project. Lowest homework grade will be dropped. The lowest midterm grade will be replaced by final if your final project score, in percentage, happens to be more than the lowest midterm score.

Letter Grade E D- D D+ C- C C+ B- B B+ A- A A+ Percentage 0-59 60-62 63-66 67-69 70-72 73-76 77-79 80-82 83-86 87-89 90-92 93-96 97-100 Disability Statement The University will make reasonable accommodations for persons with documented disabilities. Student need to register with Disability Resource Services (DSR) every semester they are enrolled for classes. DRS is located in counseling & Support Services, 2157 UC. To be assured of having services when they are needed, students should register no later than the end of add/ drop deadline of each term. Visit the DSR website at:.