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.

Interesting course, however, if you don't already have a STRONG background in math and statistics, you will get lost after about half of the first week! Some videos are just throwing formulas all over the place, without really understand what they are doing. Some are more detailed. Overall, interesting. But I think I got only 60% of the knowledge. Might have to retry later, or maybe do a different one. A glossary document will have been a nice to have (MA, AR, E, V, ACF, iid, ARM, AIC, etc.)

I liked the emphasis on implementing the theory on data within R as this knowledge is directly transferable to real life application. A good way to go from 0 time series analysis knowledge to a base that can be the starting point for further learning. I noticed occasional errors in the material (EG. one quiz question asking for lambda() whereas it meant gamma()), but nothing that significantly takes away from the learning experience. Thank you for providing this quality course for free.

The course teaches the mathematics behind SARIMA - Seasonal Autoregressive integrated moving average, and SES - Simple Exponential Smoothing. If you want to understand the mathematics behind these methods for time series analysis - this is a 5 star course. If you want examples of programming them in R - 5 stars. If you're looking for practical examples - there are practical time series examples in the second half of the course - but not many.

It was a great course. It gave me a firm foundation in time series and its application to forecasting. While some of the lectures were quite "mathy", I hanged in there and at the end day everything made a lot of sense. I now have a clear understanding of the underlying principles and I can explain the inputs and outputs of the models a colleague in my own words. I would say it was a worth investment both in terms of time and the $40 bucks!

Course provided solid understand of Time Series Analysis. I wasn't expecting to see so much math, but glad they included it. Recommend you study up on Time Series math before taking course. All of the math was converted into R algorithms for you and/or R function calls to packages that supported the concepts. I was immediately able to start applying my knowledge to my day job as a Software Engineer / Data Analyst.

A very thorough course covering almost all the topics related to time series along with the math involved. However, the explanation for the math involved was not very elaborate and easy to understand. A lot of parts were skipped in the explanation. Moreover, the examples taken to explain were simpler, rather than taking a tough example and covering all the corners. Overall, a great course but not for a layman.

Good course if you need to gain a general idea about time-series models, and forecasting concepts. I was afraid to hear words like 'ARMA', 'ARIMA' models in the research papers. After this course, I know what those papers talk about when they refer to these terms. If you need to apply this knowledge for some project, you might need to practice more than the practice quizzes in the course.

When I started this course, I wanted to get familiar with Time series analysis, particularly the forecasting aspect. I am happy to say that I am satisfy. The explanation are very clear and I was able to follow without any prior knowledge in time series analysis. The only problem I observed is that the materials are out of date. In fact, some links do not work anymore.

The instructors adopted a very practical approach to teaching the core concepts in Times Series. They did a great job using examples to analyze the data and explain the concepts. However, I do think the lectures are somewhat disorganized in some chapters so that some concepts are used before they are mentioned. Overall speaking, this is a great class.

A gentle introduction into this important area of Data Science & Analysis. The lectures take care in explaining the salient points of the topics and provide a lot of practical insights (tips & tricks) which enable students to carry out these analyses themselves. The qualifier "Practical" in the Course Title is well-deserved.

Thank you very much...

A very good course that covers both the theoretical Mathematics and the practical implementation in R.

I feel more confident now on time series analysis. I learned how to do forecasts, to interpret PACF and ACF and understand ARIMA and SARIMA.

A few small sections in the video went too quickly, but the material overall is excellent.

Good course! It gives good bases to understand and use time series models. However, until week 3 you will not see much of the 'practical' part of the course. The first weeks are mostly math related to time-series. Later on (especially week 6), the course offers a more practical side with examples, interpretation, and forecasting.

The course is well-paced and assumes very little background. This means that the first few weeks will not seem very practical, but that is necessary to lay the foundation for what is to come. My only criticism is that some of the tools in R seem out of date. A refresh of the course using tidyverse tools would be a 5-star course.

The course was hard for me because of the Math and R language. I almost gave up at Week 3 onwards and was behind for a week before I picked up again. Instructor Thistleton's careful instructions and well organized notes were really helpful. Overall I learnt a lot about Time Series, modelling, interpreting and using the R script.

Sometimes you would have to read the documents or watch the video again to grasp the idea, but overall, the two instructors introduced the contents very clear. I like how the examples of actual time series data were given in the lectures, those examples did help get a better picture of what those time series models are for.

It's a great course and it will give a great overview of the main forecasting techniques. However, the course is not THAT practical, since there were a lot of lessons talking about the theoretical concepts instead of coding. If don't have a good understanding of Algebra, you might get lost throughout this course