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STAT -560-Time Series Analysis- Winter 2016

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Forecasting, Time Series, And Regression , 4th Edition by Bowerman, O'Connell and Koehler

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

    Homework


    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.

    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 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 here for windows and here for Mac. After Installing R: click R Studio 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: 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.

    Academic Integrity


    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

    Safety


    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

    Tentative Academic Calender


    Date Chapters/SectionsTopics 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)

    Homework



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

    R-labs



    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

    Extra Resources


    StatSci.org. A good resource for varieties of data sets. These data sets are open to public and you can use these data sets for your own projects. If you happen to use these data please do not forget to mention the source.
    Online WHats New in Econometrics This a very helpful resource for advanced Time Series concepts.
    OpenItro This is an excellent resource for introductory statistics. Apart from lecture notes they also have well explained examples with R code.
    Distribution Calculator An excellent App to understand binomial distribution normal approximation to binomial
    Install R and R Commander Guideline to download and install R
    Exploratory Data Analysis Wide range of statistical topics are covered in this web page with video lectures and other supplementary materials.
    Statistics Online Computational resources interactive apps to calculate different statistical measures.
    More Stat Apps Wonderful collection of Statistics Apps for data visualization
    Machine Learning repository UCI Machine Learning Repository- a comprehensive webpage with varities of data sets.
    Financial Time Series Data A good collection of financial data

    Fuel Economy Data Fuel economy data by U.S. department of energy
    List of data A rich collection of data
    a href= " http://www.theguardian.com/sport/datablog/2012/aug/10/olympics-2012-list-medal-winners#height">Data Journalism Open data sets by British newspaper the guardian.
    Time Series Data Library A comprehensive source of Time series data with visualization
    Markdown Themes Appearance and Style themes to create HTML document using R Studio.
    Dairy Data A wonderful collection of dairy data
    Shiny Apps A comprehensive Resource of Shiny Apps
    Neural network Time Series Forecassting with Neural Network