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Time Series Analysis

Georgia Institute of Technology via edX

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Time Series Analysis has wide applicability in economic and financial fields but also to geophysics, oceanography, atmospheric science, astronomy, engineering, among many other fields of practice. This course will illustrate time series analysis using many applications from these fields.

In this course, students will learn standard time series analysis topics such as modeling time series using regression analysis, univariate ARMA/ARIMA modelling, (G)ARCH modeling, Vector Autoregressive (VAR) model along with forecasting, model identification and diagnostics. Students will be given fundamental grounding in the use of such widely used tools in modeling time series.

Throughout this course, students will be exposed to not only fundamental concepts of time series analysis but also many data examples using the R statistical software. Thus by the end of this course, students will also be familiar with the implementation of time series models using the R statistical software along with interpretation for the results derived from such implementations.

This class is more about the opportunity for individual discovery than it is about mastering a fixed set of techniques.


Weeks 1-3: Introduction to basic concepts of time series analysis

Weeks 4-6:
Introduction to the ARMA Modeling and its extension, including illustration with data examples

Week 7:
Midterm 1 Examination

Weeks 8-10: Introduction to most popular multivariate time series model, the VAR model, with data examples

Weeks 11-13: Introduction to GARCH modeling for heteroskedasticity, with data examples

Week 14: Midterm 2 Examination

Weeks 15: Overview of the time series models introduced in this course along with brief description of other time series methods

Week 16: Final Examination

Taught by

Nicoleta Serban


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Reviews for edX's Time Series Analysis
2.0 Based on 4 reviews

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1.0 a month ago
Anonymous completed this course.
This is a review of the 2018 fall session. This course is a joke. The instructor is basically reading the notes, showing at the same time slides busy with formulas, which are never rigorously derived. The homeworks consist of yes/no questions that do not include any analysis problems that would require writing a code or trying to understand the learned material. The questions are of the type "If the mean of a time series depends on the time, then the sequence is stationary. Yes or No?" There is no feedback after the homework (at least in Audit mode): you only know whether the answer was yes or…
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1.0 a month ago
Anonymous completed this course.
I take this course in 2018 Fall. I totally agree with the review above, the instructor just read the slides and basically no explanation for the content. For the homework, you can only know the answers and no further reviews. The most important is that the due date of mid-exam is different with the syllabus, and no one will answer your question in discussion board. That is ridiculous, I am regret to pay for this course.
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3.0 a month ago
Anonymous completed this course.
Agree with the detailed review above. No explanation to homework solutions. However, I appreciated plenty of good examples with R code! Thanks instructors anyway.
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3.0 4 weeks ago
Anonymous completed this course.
This course aims to be theoretical in a way since there's lots of somewhat complicated formulas with notations, yet little is done to derive these formulas for students. So it's like an unhappy medium; the course is neither rigorous theoretically or practical for those getting started with time series analysis. I thought about quitting the course earlier on in the semester, but decided to stick with it and I am glad I did. I did learn a few things that I had forgotten or not learned in my time series and forecasting courses during my degree. I audited the course, and completed all assignments and tests. I am very happy I didn't pay since I would have felt it was a waste of $99 (on EdX).

The bright spots of the course are the R examples. If you are looking to understand theory, it may be better to take another course or read a good textbook. If you are looking for practical time series analysis, try DataCamp and/or Rob Hyndman's book Forecasting: Principles and Practice.
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