STAT -460-Time Series Analysis- Winter 2017
Forecasting, Time Series, And Regression , 4th Edition by Bowerman, O'Connell and Koehler
Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: MW 2:00 PM - 3:15PM
Meeting Location: 2048CB
Monday 12:30 PM-1:30 PM
Wednesday 4:30PM- 5:45 PM
and by appointments
Course Description and Objectives
This course covers topics in time series analysis and statistical techniques for forecasting. These are time series regression, decomposition methods, X-12-ARIMA seasonal adjustment methods, exponential smoothing, and the Box-Jenkins forecasting methodologies.
Principle objective of the course is to introduce graduate and advanced undergraduate students in mathematics, economics, business, engineering, and any otherdecipline, to some of the many approaches of analyzing time series data. In addition, students will equipped with the tools and knowledge to make forecasts obtained from the statistical analysis of historical data.
Student Leanrning Outcomes:
At the end of the course, the student will be able to
analyze time series data using various statistical approaches.
generate reasonable forecast values.
assess forecast accuracy by different measures of statistics.
Forecasting, Time Series, And Regression, 4th Edition, Bowerman, O'Connell, Koehler; ISBN-13: 978-0534409777, Brooks/ Cole . We will be covering chapters 1, 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 Forecasting: principles and practice
- Markidakis, Wheelwright, and HyndmanForecasting: Methods and Applications. 3rd Edition.
- John E. Hanke, Dean Wichern Business Forecasting (9th Edition).
- Rober H. Shumway, David S. Stoffer Time Series Analysis and Its Applications.
At least five sets of homework problems will be assigned. Some additional homework, especially R-programming, problems will periodically be assigned during the lecture. Lowest homework grade will be dropped. For a better grade you need to master all the homework problems.
There will be two mid-term exams, two mini projects and a final project. To answer the exam questions, you are expected to have a clear mathematical reasoning of the statistical methods used to solve the subject problems. Final project is designed on top of Project that explore little more than what is covered in the class will result with significant grade benifits.
For a good project, you need to describe the data, pose reasonable hypotheses, select appropriate time series model/s, compute the test results, present a clear model diagnostics, 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 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 13|
|Exam II (20%)|| Wednesday, March 29 |
| Mini Project I(10%) || Due, February 20 |
| Mini Project II(10%) || Due, April 07 |
| Homeworks (15%) || TBD |
| Final project 25%) || |
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.
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 encouraged to program 911 and UM-Dearborn’s University Police phone number (313) 593-5333 into personal cell phones. In case of emergency, first dial 911 and then if the situation allows call University Police.
The Emergency Alert Notification (EAN) system is the official process for notifying the campus community for emergency events. All students are strongly encouraged to register in the campus EAN, for communications during an emergency. The following link includes information on registering as well as safety and emergency procedures information: .
If you hear a fire alarm, class will be immediately suspended, and you must evacuate the building by using the nearest exit. Please proceed outdoors to the assembly area and away from the building. Do not use elevators. It is highly recommended that you do not head to your vehicle or leave campus since it is necessary to account for all persons and to ensure that first responders can access the campus.
If the class is notified of a shelter-in-place requirement for a tornado warning or severe weather warning, your instructor will suspend class and shelter the class in the lowest level of this building away from windows and doors.
If notified of an active threat (shooter) you will Run (get out), Hide (find a safe place to stay) or Fight (with anything available). Your response will be dictated by the specific circumstances of the encounter.
Tentative Academic Calender
| Week ||Chapters/Sections||Topics covered ||Remarks |
| Week 1 and 2 (Jan 9, 11, 18) || Chapter 1 || Introduction to Forecasting|
| Week 3 (Jan 23, 25, 30) || Chapter 6 || Time Series Regression (sections: 6.1, 6.2, 6.4)|| |
| Week 4 (Jan 30, Feb 01) || Chapter 6 || Time Series Regression (sections: 6.4, 6.5,)|| |
| Week 5 (Feb 06, 08) || || Review; Exam I || |
| Week 6 (Feb 13, 15) || Chapter 7 || Time Series Decomposition || |
| Week 7 (Feb 20, 22) || Chapter 7 || X-12-ARIMA Seasonal Adjustment Method || |
| Week 8 ( Feb 27, Mar 01) || || Spring recess || |
| Week 9(Mar 06, 08) || Chapter 8 || Exponential Smoothing (sections: 8.1, 8.2, 8.3) || |
| Week 10 (Mar 13, 15) || Chapter 8 || Exponential Smoothing (sections: 8.4, 8.5) || |
| Week 11 (Mar 20, 22) || Chapter 9 || Non Seasonal Autoregressive (ARIMA) Models (sectionss: 9.1, 9.2) || |
| Week 12(Mar 27, 29) || Chapter 9, 10 || Review; Exam || |
| Week 13 (Apr 3, 5) || || ARIMA, Model Diagnostic || |
| Week 14 (Apr 10, 12) || Chapter 11 || Box-jenkins Seasonal Modeling (ARIMA) || |
| Week 15 (Apr 17, 19) || Chapter 12* || Advanced Time Series Models || |
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.
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
A comprehensive list of data A rich collection of data
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
DataCamp A comprehensive resourse to learn Statistics and Data Science
Time Series Analysis and its Application R-code with description of Shumway and Stoffers book