STAT -560-Time Series Analysis- Winter 2016

Meeting Times:

Email:

Meeting Location: 2046CB

Fi Office: 2087CB

Monday 10:30 AM- 12:00PM

Wednesday 5:00 PM- 6:00PM

Friday 10:30 AM- 12:00PM

and by appointments

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.

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.

At the end of the course, the student will be able to

Forecasting, Time Series, And Regression, 4th Edition, Bowerman, O'Connell, Koehler; ISBN-13: 978-0534409777, 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.

- R. Hyndman and George Athanasopoulus
**Forecasting: principles and practice** - Markidakis, Wheelwright, and Hyndman
**Forecasting: Methods and Applications. 3rd Edition.** - John E. Hanke, Dean Wichern
**Business Forecasting (9th Edition).** - Diez, Barr, and Cetinkaya-Rundel,
**OpenIntro Statistics.**

At least five sets of homework problems will be assigned. Some addition homework problems will periodically be assigned during the lecture. Good news! lowest homework grade will be dropped. For better exam results you need to master all the homework problems.

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.

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.

We use a software called "R". R is a programming language for statistical computing and visualizing data. It can be downloaded for free from http://www.r-project.org. We will R Studio for regular classroom activities. R studio is an open source Integrated development Environment(IDE) for R. To download R click

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) |

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 |

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: webapps.umd.umich.edu/aim. If you have disability that necessitates an accommodation or adjustment to the academic requirements stated in this syllabus, you must register with DRS as directed above and notify me. Upon receipt of your notification, we will make accommodation as directed by DRS.

￼The University of Michigan-Dearborn values academic honesty and integrity. Each student has a responsibility to understand, accept, and comply with the University's standards of academic conduct as set forth by the Code of Academic Conduct ￼(mdearborn.edu/policies_st-rights), as well as policies established by each college. Cheating , collusion, misconduct, fabrication, and plagiarism are considered serious offenses, and may be monitored using tools including but not limited to TurnItIn. Violations can result in penalties up to and including expulsion from the University. At the instructor's direction, the penalty may be a grade zero on the assignment up to and including recommending that student be expelled from the University. It is the sole responsibility of the student to understand and follow academic guidelines regarding plagiarism. The University of Michigan-Dearborm has an online academic integrity tutorial that can be accessed at: umdearborn.edu/umemergencyalert

All students are strongly encouraged to register in the campus Emergency Alert System, used to communicate with campus community during emergency. More information on the system and how it works, along with enrollment information can be found at: webapps.umd.umich.edu/aim

Date | Chapters/Sections | Topics covered | Remarks |

January 6 | Review | ||

January 11 | Chapter 2 | Basic Statistical Concepts | Rlab |

January 13 | Chapter 2 | Confidence Interval; Hypothesis testing | |

January 18 | No Class | MLK Day | |

January 20 | Chapter 1 | An Introducion to Forecasting | |

January 25 | Chapter 3 | Simple Linear Regression: Point Estimates and Point prediction | |

January 27 | Chapter 3 | Simple Linear Regression: Confidence and Prediction Interval | |

February 1 | Chapter 4 | Multiple Linear Regression | |

February 3 | 4.3, 4.4,4.5 | Mean Square Error, Test of Significance, predicion Intervals | |

February 8 | 4.6,4.7, 4.7, 4.8 | Quadratic Regression Model, Interaction | |

February 10 | 4.9, Review | Qualitative Independent Variables | |

February 15 | Exam 1 | ||

February 17 | 5.1, 5.2 | Model Building, Multicollinearity, Residual Analysis | |

February 22 | 5.3, 5.4 | Detecting outlying observation and Influential Observations | |

February 24 | 6.1, 6.2 | Modeling Trend by Polynomial functions, Detecting Autocorrelation | Rlab |

February 29- March 04 | Break | ||

March 07 | 6.3, 6.4 | Seasonal variation | |

March 09 | 6.5, 6.6 | First-Order Autocorrelation | |

March 14 | 7.1, 7.2 | Multiplicative Decomposition, Additive Decomposition | |

March 16 | 7.3 | X-12-ARIMA Seasonal Adjustment Method | |

March 21 | 8.1, 8.3 | Simple Exponential Smoothing | HW 5 Due |

March 23 | 8.5, 8.6 | Holts Winters Method, Damped Trend and Other Exponential Smoothing Method | |

March 28 | Review | ||

March 30 | Exam 2 | ||

April 4 | 9.1.9.2 | Stationary and non-stationary time series | |

April 6 | 9.3, 9.4 | Nonseasonal Box-jenkins Models | |

April 11 | Chapter 11* | Box-jenkins seasonal Modeling | |

April 13 | Review | ||

April 18 | Review | ||

April 27 | Final Exam (3:00AM- 6:00 PM) |

| Description | Remarks |

Chapter 3: 3.1, 3.3, 3.4, 3.10, 3.12, 3.23, 3.35 | Due Wed Feb 03 | |

| Description | Remarks |

review: Hypothesis Testing | ||

Data Visualization | ||

Measures of Data | ||

Normal Distribution | ||

Central Limit Theorem | ||

Book Data | Download | |

More Data | Download | |

Old Auto Data | Download | |

Texas Oil Data | Explore |

Exploratory Data Analysis Wide range of statistical topics are covered in this web page with video lectures and other supplementary materials.

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