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Practical Time Series Analysis, 纽约州立大学纽约州立大学

454 個評分
122 個審閱


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!...


創建者 JM

Mar 21, 2019

This was a very good and detailed course. I liked this course for two reasons mainly:\n\nIt started from the basics of timeseries analysis, covering theory and secondly it took me gradually to r.

創建者 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.


122 個審閱

創建者 Indresh Tiwari

Apr 12, 2019

Course is really good to know the in depth understanding of the various models working around for the time series analysis.

創建者 Hany Nagaty

Apr 11, 2019

Great course, good balance between theory & practical applications.

For some lectures, the slides are not provided (pdf notes are provided instead). I would preferred to have the PDF sliders of the Power Point as well.

創建者 Anisha Singhal

Apr 09, 2019

It is full of theories, derivations and equations. I took this course because I have to work on forecasting project at my company. But this course has not helped me much with practical implementation yet (I have completed 3 weeks of learning till now). I have already spent weeks on learning this course with no effective gain from it. Udemy course on Time Series is better than this. I wish I had started with that!

創建者 Xinrui Peng

Apr 01, 2019

Great course. It's much better than my professor's lectures

創建者 Heberto Suarez-Roca

Apr 01, 2019

There was not a good intuitive and more visual explanations of the principles behind the techniques.

Given the proposed 'practical' nature of the course, it would be better to explain any concept by using concrete every-day examples than preceding them with a elaborated mathematical reasoning of the equations used.

創建者 Murali

Mar 30, 2019

very good explanation. specifically about ACF and PACF.


創建者 William Gallego

Mar 29, 2019

This course is great. It is the best that I have tried on the web. Clearly explained and the math is fair to understand the topics and be able to get the ideas clear. Even the code for R is a great way to learn!

創建者 Aikaterini Lymperidou

Mar 26, 2019

Very helpful practical easy exercises to get to know better the Time series and their analysis. I totally recommend it.

創建者 Yash Gandhi

Mar 25, 2019

Amazing course

創建者 Cesar Nebot

Mar 24, 2019

Very useful and practical course. Absolutely recommendable.