Zurück zu Practical Time Series Analysis

4.6

Sterne

926 Bewertungen

•

257 Bewertungen

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!...

Jan 24, 2020

Excelente, uno de los mejores cursos que he tomado. Lo más importante es que se practica muy seguido y hay examenes durante los vídeos. Si hay un nivel más avanzado de este tema, seguro que lo tomo.

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.

Filtern nach:

von Ahtsham H

•Mar 28, 2020

a very good course

von Rashid P

•Jul 09, 2019

very useful course

von Ruiliang L

•Oct 07, 2018

Easy to understand

von Swarn

•Feb 24, 2020

Excellent class!

von Yuan C

•Apr 05, 2018

Very nice course

von EDILSON S S O J

•Jun 06, 2019

Amazing Course!

von Marek D

•Jan 27, 2020

Great content!

von CHENG Y T

•Jun 28, 2018

Very practical

von Deleted A

•Jun 05, 2018

Amazing course

von 刘思航

•Oct 24, 2018

very helpful!

von Yu Q

•Jul 18, 2018

Very helpful!

von Juan L R A

•May 27, 2020

Nice course

von Roberto G A

•May 23, 2019

Excellent

von Douglas B P

•Sep 02, 2018

Great!!

von Cathy D

•May 23, 2018

useful!

von Alla E G

•Jan 18, 2020

Thanks

von Scott S

•Apr 29, 2018

Overall, this was a really great class. I have a good grasp now for the basics of modelling time-series and producing forecasts (using ARIMA, SARIMA, Smoothing with Holt-Winters). I also have an understanding of the tools used for analysing time-series (autocorrelation, partial-autocorrelation).

The course is a good mix of theory and practice. I haven't taken a stats class for a long time; however, the first week was more than enough to refresh my memory. Really, only some basic stats knowledge is needed (mean, covariance, correlation). There is also quite a lot of usage of the geometric series. However, a lot of time is spent reviewing this topic later (week 3, I think). The course is done in R, but the usage is so small that anyone with any experience in programming will have no trouble. I actually had no experience with R before this course, and I finished the course with only a little additional experience.

My only complaint is that the course could use some minor maintenance. One of the weeks (maybe 3?) has a section of duplicated notes, but I'm pretty sure that the section with duplicated notes could be deleted. Most of the pdfs have the wrong week in the header (likely a new week was added after the course was released). There are also several quiz questions with incorrect values (before taking a quiz, I recommend scanning through the forum to find known problems). The instructors do occasionally post in the forums, so that gives me hope that the course will not be abandoned.

von David R

•Nov 09, 2019

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.

von Ron C

•Sep 09, 2019

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.

von Matteo B

•May 25, 2020

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.

von Jose L A

•Nov 18, 2018

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

von Carlos R P G

•Jan 08, 2020

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.

von Stefnir H K

•Dec 30, 2019

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.

von River B

•Nov 25, 2019

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.

von Marc-Andre C

•Apr 21, 2018

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.

- KI für alle
- Vorstellung von TensorFlow
- Neuronale Netzwerke und Deep Learning
- Algorithmen, Teil 1
- Algorithmen, Teil 2
- Maschinelles Lernen
- Maschinelles Lernen mit Python
- Maschinelles Lernen mittels Sas Viya
- R-Programmierung
- Einführung in die Programmierung mit Matlab
- Datenanalyse mit Python
- AWS-Grundlagen: Mit der Cloud vertraut werden
- Grundlagen der Google Cloud-Plattform
- Engineering für Site-Funktionssicherheit
- Englisch im Berufsleben
- Die Wissenschaft des Wohlbefindens
- Lernen lernen
- Finanzmärkte
- Hypothesenüberprüfung im öffentlichen Gesundheitswesen
- Grundlagen für Führungsstärke im Alltag

- Deep Learning
- Python für alle
- Data Science
- Angewandte Datenwissenschaft mit Python
- Geschäftsgründungen
- Architektur mit der Google Cloud-Plattform
- Datenengineering in der Google Cloud-Plattform
- Von Excel bis MySQL
- Erweiterte maschinelles Lernen
- Mathematik für maschinelles Lernen
- Selbstfahrende Autos
- Blockchain-Revolution für das Unternehmen
- Unternehmensanalytik
- Excel-Kenntnisse für Beruf
- Digitales Marketing
- Statistische Analyse mit R im öffentlichen Gesundheitswesen
- Grundlagen der Immunologie
- Anatomie
- Innovationsmanagement und Design Thinking
- Grundlagen positiver Psychologie