FA18
Time Series Analysis
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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.
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| Rating | 2.0★ based on 4 ratings |
|---|---|
| Length | 15 weeks |
| Effort | 8 - 10 hours per week |
| Starts | Aug 20 (296 weeks ago) |
| Cost | $0 |
| From | GTx via edX |
| Instructor | Nicoleta Serban |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science |
| Tags | Data Analysis & Statistics |
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What people are saying
like an unhappy medium
So it's like an unhappy medium; the course is neither rigorous theoretically or practical for those getting started with time series analysis.
include any analysis problems
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.
completed all assignments
I audited the course, and completed all assignments and tests.
forecasting courses during
I did learn a few things that I had forgotten or not learned in my time series and forecasting courses during my degree.
neither rigorous theoretically
never rigorously derived
The instructor is basically reading the notes, showing at the same time slides busy with formulas, which are never rigorously derived.
those getting started
thought about quitting
I thought about quitting the course earlier on in the semester, but decided to stick with it and I am glad I did.
2018 fall session
This is a review of the 2018 fall session.
audit mode
There is no feedback after the homework (at least in Audit mode): you only know whether the answer was yes or no.
appreciated plenty
However, I appreciated plenty of good examples with R code!
but decided
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| Rating | 2.0★ based on 4 ratings |
|---|---|
| Length | 15 weeks |
| Effort | 8 - 10 hours per week |
| Starts | Aug 20 (296 weeks ago) |
| Cost | $0 |
| From | GTx via edX |
| Instructor | Nicoleta Serban |
| Download Videos | On all desktop and mobile devices |
| Language | English |
| Subjects | Data Science |
| Tags | Data Analysis & Statistics |
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