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102 avis

Welcome to Practical Time Series Analysis!
Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.
In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.
Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn.
You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself!
Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!...

par MS

•Feb 28, 2018

I have not completed the course yet, working on week 5. If you have some Math background, this course gives a good practical introduction to Time Series Analysis. I recommend it.

par RS

•Mar 18, 2018

Really great lectures and clearly explaining the concepts and complicated models. In my opinion, a bit of practical applications of these models on Panel Data should be included.

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103 avis

par Kyle Conniff

•Feb 20, 2019

I only wanted to look at a few lectures from this course, but they were so good that I decided to go through the rest of it. I was not let down.

par Dongliang Zhou

•Feb 15, 2019

Terrific! Thanks the teachers! Both of You are very good. You make the complexity easy to learn.

Nice theory, Nice example, Nice code, Nice pdf files.

I will definitely review this course again and again whenever I need to use time series to forecast.

par Denis Fernando Martinez

•Feb 14, 2019

I liked it a lot and it helped me a lot to understand the topic that I am going to use in my thesis.

It has a little messy some classes and topics so they should revise some videos.

par Jie Feng

•Feb 13, 2019

very good course for time series study

par Jesús Conesa

•Feb 03, 2019

Good course to learn the basic concepts about time series. In my opinion there should be more practical exercises. They force you to better understand the theory and are always a good idea to really master any subject.

par 李欣儒

•Feb 02, 2019

some mistakes in the handout, but still learn lots of useful knowledge and skill from this class.

par Matthew C Ziegler

•Jan 28, 2019

Great course, I highly recommend!

par anas

•Jan 27, 2019

A Well structured and well taught course thank you !

par Sai Raghuram

•Jan 27, 2019

First I started out reading Intro to Time Series and Forecasting, the book suggested by everyone. But, I could not understand the math because it was too tough. I did not lose hope. I completed this course because sometimes you need to get an overview of what needs to be done and then if you dive into the math of it, it will be easy. Much recommended course for the beginning of time series and forecasting techniques. 5 stars! Thank you

par Jerry Handford

•Jan 11, 2019

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.

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