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NJIT Academic Integrity CODE:  All Students should be aware that the Department of Mathematical Sciences takes the University Code on Academic Integrity at NJIT very seriously and enforces it strictly.  This means that there must not be any forms of plagiarism, i.e., copying of homework, class projects, or lab assignments, or any form of cheating in quizzes and exams.  Under the University Code on Academic Integrity, students are obligated to report any such activities to the Instructor.

 

 

Number of Credits:  3

 

Course Description:  An introduction to applied univariate time series analysis. Topics include regression techniques for modeling trends, smoothing techniques (moving average smoothing, exponential smoothing), autocorrelation, partial auto-correlation, moving average, and autoregressive representation of series, Box-Jenkins models, forecasting, model selection, estimation, and diagnostic checking, Fourier analysis, and spectral theory for stationary processes.

Prerequisites: Math 341 with a grade of C or better or Math 333 with a grade of C or better

 

Textbook:  Introduction to Time Series and Forecasting, by Peter J. Brockwell and Richard A. Davis; Publisher: Springer, 2nd edition (2002); ISBN-978-0-387-95351-9

Reference: Time Series Analysis and Its Applications With R Examples, Second Edition, by Shumway and Stoffer published by Springer.

Instructor:   (for specific course-related information, follow the link below)

 

Math 447-001

Prof. Pole

 

Grading Policy:  The final grade in this course will be determined as follows: 

▪ Homework:	20%
▪ Project:	20%
▪ Midterm Exam:	25%
▪ Final Exam:	35%

 

Your final letter grade will be based on the following tentative curve:

A	90-100	C	70-74
B+	85-89	F	0-69
B	80-84
C+	75-79

 

 

Drop Date:  Please note that the University Drop Date November 6, 2012 deadline will be strictly enforced.

Course Material:  This class is an introduction to time series modeling, estimation and forecasting. It will be a blend of hands on applications and underlying principles. Topics covered include: time series models, smoothing techniques (e.g., exponential smoothing), trend analysis, seasonality and trading day effects, naive forecasting models, stationary and ARMA models, autocorrelation, partial autocorrelation, moving average, autoregressive representation of series, estimation and forecasting for ARMA models. In addition, the theory of solving difference equations will be developed.

Grading Policy:  Homework problems will be assigned regularly. Some of these assignments may require the use of statistical software for their solution. You must work out not only these problems but also practice similar problems and learn the techniques needed to solve them.

Class Rules, Department and University Policies:

• No assignments, home works, exams will be accepted late.

• Any complaints regarding grading have to be presented right away after the return of the papers. A certain number of exam papers are photocopied and stored, before being returned, for official record purposes.

• Please, always bring a statistics calculator to your quizzes, exams and to all the lectures.

• Looking into your neighbors work during exams is not allowed. Hats, caps, etc., that keep the eyes hidden from the proctor but not from the neighbors work during exams are not allowed.

• The students should refresh themselves before the exam starts and the students are generally not allowed to use rest rooms during exams.

• There will be no make-up tests, make-up quizzes and homework. Late homework is not acceptable. In case of an extenuating circumstance such as illness, etc., proof that can confirm the excuse is genuine must be produced.

• Attendance at all classes and tests is required.

• The use of cell phones is not allowed under any circumstances.

• No eating allowed during the class and exams periods.

• You are expected to remain in the classroom for the entire class period. Wandering in and out of the classroom is not allowed.

Attendance and Participation:  Students must attend all classes. Absences from class will inhibit your ability to fully participate in class discussions and problem solving sessions and, therefore, affect your grade. Tardiness to class is very disruptive to the instructor and students and will not be tolerated.

Makeup Exam Policy:  There will be No make-up EXAMS during the semester. In the event the Final Exam is not taken, under rare circumstances where the student has a legitimate reason for missing the final exam, a makeup exam will be administered by the math department. In any case the student must notify the Math Department Office and the Instructor that the exam will be missed and present written verifiable proof of the reason for missing the exam, e.g., a doctors note, police report, court notice, etc., clearly stating the date AND time of the mitigating problem.

Further Assistance:  For further questions, students should contact their Instructor. All Instructors have regular office hours during the week. These office hours are listed at the link above by clicking on the Instructor’s name. Teaching Assistants are also available in the math learning center.

Cellular Phones:  All cellular phones and beepers must be switched off during all class times.

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MATH DEPARTMENT CLASS POLICIES LINK 

All DMS students must familiarize themselves with and adhere to the Department of Mathematical Sciences Course Policies, in addition to official university-wide policies. DMS takes these policies very seriously and enforces them strictly. For DMS Course Policies, please click here.

September 3, 2012	M	Labor Day ~ No classes
November 6, 2012	T	Last Day to Withdraw from this course
November 20, 2012	T	Classes follow a Thursday Schedule
November 21, 2012	W	Classes follow a Friday Schedule
November 22-25, 2012	R-Su	Thanksgiving Recess
December 13, 2012	R	Reading Day
December  14-20, 2012	F- R	Final Exams

 

Course Outline and Homework Assignments:

 

Week	Sections	Topic
Week 1	Appendix A Sections 1.1 – 1.2	Review of probability Introduction to Time Series (I)
Week 2	Sections 1.3 – 1.5	Introduction to Time Series (II)
Week 3	Section 1.6	Introduction to Time Series (III)
	Section 2.1	Stationary Process (I)
Week 4	Sections 2.2 – 2.3	Stationary Process (II)
Week 5	Sections 2.4-2.5	Stationary Process (III)
Week 6	Sections 2.5 – 2.6	Stationary Process (IV)
Week 7	Sections 3.1-3.2	ARMA Models (I)
Week 8	Section 3.2	ARMA Models (II)
		MIDTERM EXAM
Week 9	Sections 3.2-3.3	ARMA Models (III)
Week 10	Section 3.3	ARMA Models (IV)
	Section 5.1	Modeling and Forecasting with ARMA Processes (I)
Week 11	Sections 5.1-5.2	Modeling and Forecasting with ARMA Processes (II)
Week 12	Section 5.3	Modeling and Forecasting with ARMA Processes (III)
Week 13	Sections 5.4-5.5	Modeling and Forecasting with ARMA Processes (IV)
Week 14	Section 5.5	Modeling and Forecasting with ARMA Processes (V)
Week 15	Section 6.1	Introduction to Nonstationary Time Series
Week 16
		FINAL EXAM

 

Prepared By:  Prof. Andrew Pole

Last revised:  August 29, 2012