Perfect

perfect

Great!!

useful!

Thanks

good

good

Good

I'm in week 5, and I think that this course is interesting and you learn from it. However it is done in a somewhat sloppy manner, to my taste.

My biggest problem is the notations and equations are a bit of mess. Beta's in one equation are replaced with phi's in another (sometimes in the same "lecture" slides) or theta's - there's just no real coherent notation. The formulas are brushed through, and they contain mistakes (a product of this sloppy notations), e.g. pi(beta) is missing the beta (which is what it depends on! week5, ARMA properties and a little theory). The R code is also sloppy, for example you see them setting variables in the first cell, and then never using them in the next cell. Or calculating variance using a cumbersome call to an acf function telling it to bring back the autocovariance, and taking the first term. TL;DR - It's just sloppy.

There are no exercises, but the quizzes contain some code you can run. Not enough for really drilling the material into you, though.

In general, I think this course could really improve, and I would like to see it do so. As a general introduction to the topic it might be decent enough.

​This course is difficult as it's not the typical hands on practice with R that you can find anywhere on the web but it goes deep into the math and statistics to let you see and understand what's behind the calls to the automatic routines that at the end we will use. 4 stars are motivated for two reasons: 1. there are some issues here and there with labs and R code 2. I would have preferred a direct-line with teachers and tutors because honestly there are some topics that raise questions and require clarifications but as far as I could see nobody answers questions in the community forum and therefore I found myself going outside of the course to find answers on the web or back to math books which is good if you are persistent but it slows down the overall speed of the learning and the course. So my proposal to coursera : why don't give the possibility (maybe paying for their time) to interact with teachers ?

Judging by some of the comments left in the Discussion Forums, the course name may be a bit of a misnomer. I think the term "practical" conveys more of a hands-on applied focus (using software tools to diagnose and estimate various time series), rather than a more theoretical approach. While there are numerous examples provided, there's also a sizeable theoretical component. While it's certain arguable that setting students loose with software tools and no understanding of the basis of their development is also dangerous, I think "Time Series Analysis" or "Time Series Analysis Fundamentals" might be a better title for the course.

That being said, the course met my objectives. There are a number of links to datasets that are obsolete; it would be good if these were updated, rather than having to spend time tracking them down on the Internet.

Professors obviously know their stuff and work to outline all the math fairly logical. The title, "Practical Time Series" is a little lost on the actual workload. I am finishing week 3 and I have yet to find anything 'practical' about the course. i'm very intrigued about the math, it is interesting and challenging, but i felt like the discussion in week 1 about all of the data sets we were going to use was a tease.

I would be better able to absorb (not just learn it long enough to ace the quizzes) the material if for each concept there was a practical application of the concept to one or more of the data sets that were made available to us. Because we don't, I often find myself in my own head, searching for applications, and thus not fully paying attention to the videos, which then I have to go back and watch multiple times.

This is a fantastic course, and I would recommend it to everyone that is interested in Time Series Analyses. After finishing the 5 week program, I can confidentially say that I feel comfortable to start tackling TS projects and build some forecasting models.

However, I deduct one star because the learning curve is very steep and could/should be supported more through graphs and examples, especially in the earlier part. This can be frustrating, especially for people without a very strong statistics background. My best advice for now is to keep going, many concepts become clearer in later lectures.

Overall, this course is highly enjoyable (for a statistics course on R) and I do recommend it to anyone that wants to explore the fascinating world of ARIMA models and time series.

The course gives really useful skills regarding time series analysis, but it seems a little bit forgotten by the authors since some links in the during the course are not working anymore ( for instance the link describing whether a seasonality is addictive or multiplicative "http://www.forsoc.net/2014/11/11/can-you-identify-additive-and-multiplicative-seasonality/". Also,there are time a future content is presented before the class in some questions, as is the case the moving average week where there is a question regarding auto regressive process, a content present in future classes. Besides those points, the classes and material are really helpful, and i can say that this skills learned will sure be used in my professional life

A very solid introduction to time series analysis, recommended if you have understanding of probability and statistics concepts.

I have seen some complains about the course not being practical enough. The practice comes at the last third of it, and this is as it should be. The SARIMA model is composed of 4 different models (S + AR + I + MA), if you don't understand them independently your chances of doing anything useful with it are slim.

I would have liked it to be more language agnostic since I use Python. The statsmodels module has all the time series analysis tools that you need, and allows to load R datasets, albeit not all of them. The rest you can find fairly easily googling.

This course will teach you many of the concepts of time series analysis. It's a good course that is clearly taught by experts in the field and it is no lesser than any course I have taken at a university level.

The problem with him is that the lectures are dry and feel outdated, they are not bad in any way just two professors with a webcam and slides. The second problem is that the tests are a bit easy and you can pass them without understanding by just trial and error.

Overall I recommend this course for those that have little or no background in time series but would really like to dive into this topic. I also recommend at least 1-2 years of Bs in Eng or Sci.

An excellent introductory course on time series analysis. It has an excellent blend of theory and practice and everything else became intuitive once you studied and gained intuition for the math. Some of the lower rated reviews mention too much theory, but I feel it was imperative to fully understanding the course and am glad they included it. Completing the entire course felt rewarding.

I docked one star because of the sloppiness of some of the slides and equations. Some of the examples don't work either. I enjoy William's videos as his pacing is good, but a lot of times Sadigov tries to rush through the slides as fast as possible.

Well built class. I especially enjoyed the inclusion of written material, which I find easier, faster and more enjoyable than videos usually. The material itself is well constructed and the professors are clear. The low point for me comes with the intended audience of the class. At first glance, it is directed toward professionals that have already some familiarity with time series. While I could follow the course independently, I had to rely on other resources to gain intuitions on the concepts. I still don't consider that I could explain the material that I learned as well as I wish I would.

Good introduction to time series analysis, covering the standard curricula of discrete-time stochastic processes, useful statistics, with some additional work with R and some introductory-level theory. The course is not especially rigorous and the quizzes are not hard, but as an introductory course for a person new to time series but with at least partial undergraduate mathematical background, this is a good start. I mostly read the slides to learn the material, as I prefer to read material on my own, and the slides were informative and easy to follow.

Good introduction to the fundamentals of MA and AR processes, the ACF, PACF, etc. I would have appreciated more applications, more examples showing how these tools are actually used in the real world. Suggestion: Add a week to the course where you take us through several "messy" real-world examples. For example, what are a few key research articles that have used the methods of this course and found REAL INSIGHTS about the studied system? Walk us through a few key real-world success stories of using these methods, from the research literature!

The course met my expectations, which was to develop basic skills and tools to better understand time series as a jumping off point for some of the work I am doing. I found the practical examples (e.g. coding of the solutions) to be most helpful for my learning style. Also appreciate concept development thrust, to help better understand the applicability and pitfalls of the tools. That being said, I didn't particularly find some of the mathematical derivations helpful, given my bent toward the practical application of the tools and concepts.

The two instructors explain everything crystal clear with cool mathematicians style which I enjoyed a lot!! They definitely deserve 5 stars! The 1 star I took away is because Coursera did not set a good expectation to this class, because of which I'm quite frustrated with how much maths i need to deal with and how less practical it is compared to my 7 years of experience in industry. Though after taking the course i have deeper understanding of the mechanism happening behind R existing functions - thanks to the two great instructors.

I think the course is very helpful and you learn how to perform time series analysis. In the last chapter I was missing the motivation for using tripple exponential smoothing vs. the former SARIMA model. When would I use what? I give only 4 starts due to week 3, where many complained about, and I would agree, that the motivation for the mathematical construction of the Yuri Walker equations is unclear and the lesson itself is a bit confusing. However, week 4 is then way better, when using the matrix notation and concrete examples.

After coming towards the end of the course, I have changes my perception about the course. It is a great course to learn Time Series Analysis. Though it has some advanced theory and derivations in certain lectures, it has lots of practical exercises as well to perform hands-on. It gives good understanding of time series concepts and different models associated with it. I would recommend this course. I would recommend the instructors to add ARIMAX and multivariate time series analysis in the course as well.

There are some inconsistencies with notation. The quizzes are much too easy and the code/problems are basically given to you. I would say this is a good introductory course to see if you have any interest in the subject. There is some good theory development in the weeks covering autoregressive and moving average models. I did learn some of the terminology and methods for time series analysis that will allow me to go into more developed sources with a basic intuition of some simple processes.