Great content

Very helpful

Good course

Good Course

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The course is a good opportunity to learn and/or review basic concepts on time series modeling (e.g. ARMA, ARIMA). They are presented in a way that ideas are easily understood, but also covering enough detailed aspects. I really enjoyed lectures of Professor Sadigov as he usually derives all of the formulas presented.

However, there are many things in this course that I would urgently improve. Content is not properly sorted, so you can find concepts being mentioned before being introduced or concepts being introduced twice. Moreover, I have encountered errors in both slides and quizzes. Most of them had already been spotted by other users in the forums, but they have never been corrected. Indeed, course has not been reviewed for a while, so you will often struggle to get the data used in the lectures as links provided are no longer valid. Quizzes are also very simple, most of the time.

The course is fairly good, but I struggled early on due to the complexity of the mathmatics. It was not until I came back (2 years) later to finish it off that it was easier, considering I have been working in forecasting that whole time, I didn't get much out of the course. But I thought I should just finish it off.

Overall, I would say there is too much focus on the mathmatics, and I detest the style of lecturing where the presenter explains the equation in 'mathmatical' language, which is not fully explained in the slides. It is clearly not accessable to those with 'high school mathmatics'. The learning meterials should be more explicit, and 'applied'. Also needs updating the code as its in base R, which is not used so commonly these days.

Contents of the course are very good. It provides a compact introduction to the most important statistical methods for time series estimation. Nevertheless, there are many mistakes in slides/exercises, necessity to download datasets instead of using predefined ones, and a great number of time series libraries to download when we could just use 2 of those, which is ok, but I took 1 star for it. The other star taken was because I think the course could still include more information on the theory of time series (yes, I understand that the name is PRACTICAL time series, but still), and more meaningful exercises. Most of those were pretty trivial.

Overall, it is a pretty good course: in just a few weeks you are introduced to a satisfactory level of time series analysis with R even if your background in R and/or statistics is not that high. I have personally missed some more depth (even if optional: for example, providing additional optional readings, or creating an honours track with extra difficult assignments for motivated students). Instructors are pretty good but they should make an extra effort to be clearer and not to speed up too much: some more additional videos or just more time for each video covering the same content would be better.

While this course was pretty thorough, it was way too mathematical and not nearly as applied as I would have expected a 'practical' course to be. It was also extremely frustrating that a large proportion of the forum messages went unanswered - I've never done a Coursera course before where there hasn't at least been a research assistance answering questions. You really had to try to follow everything yourself, and if you had a question, you had to find the answer yourself. A bit disappointing.

It was a nice course, very informative. But week 3 simply had way too much matter in it in comparison with the previous 2 weeks which makes it difficult if you've set a timetable. Also, week 6 had poorly maintained data availability, and missing code. Lastly, the professors are absent from the discussions. There are unanswered doubts from longer than a year ago.

But if you dedicate enough time and effort, this course is pretty good.

I think, most of the time the link between application and the theory is absent in this course. A significant emphasis is put on the formula and theory, but not enough connection to the real world. The last point, please be a story teller, and start with why?? why use this? why use that? motivate us and guide us through the process.

It's decent for what it is - a course trying to balance theory with practicality. However, it uses R for the target implementation environment. That's fine, but as a matter of taste, I prefer python. Therefore, for me, the practicality aspect of the course wasn't the greatest, and that was its main selling point.

It is not as practical as one might think. It feels like it gets too theoretical at certain points (which to me seems uncalled for as the name of the course has Practical in it) and I wish it talked about more stuff like GARCH/ARCH models, ADF tests, more recent methods to forecasting, etc...

It is in R, making it rather limited for applications in industry, however using Python's statsmodel you should be able to keep up with the notes.

I was expecting a lot more theoretical explanation of the concepts rather than just demo'ing some R code and fitting some libraries to data.

If you want to see a whole lot of proofs and have someone tell you complicated things are really simple and obvious, then this is the course for you. I wanted a class to teach me how to do the tasks at work. It spent a little bit of time on that and a WHOLE lot of time on the proofs.

It was definitely a gentle and accessible introduction to time series analysis. I was hoping for something a little more comprehensive and challenging, however, and sound quality was sometimes poor. This would serve as good preparation for an introductory course on time series.

Overall, this is a good course for beginners in time series analysis.

However, I wish they had go beyond the basics. I missed more advanced content, discussions between different forecasting techniques, multivariate forecasting...It is pretty basic.

Very interesting course. For a 'practical' course, I did find it rather theoretical. Also, some links to downloadable data are missing and there are some small errors in the R scripts that are offered. Nonetheless a good course, I learned a lot!

The coding for the steps confusing and can follow how the tutor explained. I would like to suggest for the course coordinator to create a document on steps with coding for each model or test we want to run. Thank you.

Good/thorough introduction to moving average, auto-regressive, intetgrade, and seasonal models as well as basic forecasting. Some of the labs don't work, as well as links, and the organization is messy in places.

Without covering regression with exogenous factors, I don't feel prepared to apply these concepts in practice. But it was a good overview of the theoretical fundamentals of autoregressive processes.

If you have non-technical or not much aware of stats, you will probably face problems understanding the equation algebra. I think the tutor has to explain how to calculate with a manual example.

I was interested in learning more about situations where one time series affects another, on which there was no content. Somewhat disappointed by that. Very rigorous, apart from that.

While professor Thistetlon gave the classes in a way that kept my attention,Prof Sadigov wasn't so good at it. Clearly he know a lot but not as a teacher.