Retour à Practical Time Series Analysis

4.6

étoiles

917 évaluations

•

253 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!...

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.

Filtrer par :

par Laurentiu N

•Aug 16, 2018

Terrible explanations... they do not make any sense....

Basically the instructors are reading mathematical expressions. No intuition, no significance, you are taught mechanically, sorry to say it

I wont buy anything from this provider ever.

par VB

•Jul 26, 2019

Nothing practical, real example are used to enhance theoretical stuff.

Not a single example of practical use.

Too many mistakes in course - quizes are based on following week materials, materials are titeled with mistakes, there are mistakes in narratives in addition to the mistakes in what is talked.

One of the lecturer is talking like he has to read slides as quickly as possible, because he is to buisy with other stuff.

Too much math. you have to know algebra really well to understand what he is talking about.

you should learn some R by yourself, because it is not

explained how to do a lot of things. i think R should be in cluded in course title, to make people know in advance...

my score is 1.4(9) stars....

par xun y

•Dec 16, 2018

Great introductory course on time series. Focus on ARIMA model most of the time while the last lecture capture a little bit of exponential smoothing. Would be great if there if summary lecture regarding to when to use which modeling technique. would be even better if there is a optional lecture to cover some of the more advanced time series models.

par Supratim C

•Sep 25, 2019

Very monotonous lectures. They feel like a recitation of formulae.

par Benjamin O A

•Jul 26, 2018

This is one of the best courses I've taken so far on Coursera. The exercises and delivery are so practical. I had taken a college course in Time Series Analysis, but didn't pretty much understand the concepts. This course has given me far better theoretical and practical understanding of TMS. A big thanks to the professors.

par Janki M

•Mar 21, 2019

This was a very good and detailed course. I liked this course for two reasons mainly:

It started from the basics of timeseries analysis, covering theory and secondly it took me gradually to r.

par Lingbing F

•Jul 05, 2019

a good course on univariate time series modelling by ARMA type models, with additional details on Yule Walker equations, seasonal models, and forecasting.

par Martin H A

•Mar 30, 2018

I found one of the instructors (Thistleton) much clearer and didactic than the other. I would have liked a deeper formal insight into the models that were discussed: limitations, assumptions, what kind of physical models they can represent? what to do with systems that don't behave "nicely"?, etc.

par Neel D

•Nov 15, 2019

Too much detail and outdated course

par Madhu S

•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 Heberto S

•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.

par Juan M G H

•Sep 26, 2018

On other courses I received feedback on the forums in a prompt manner from the instructors, here none of my questions have been answered.

par Kanchan K

•Nov 17, 2019

It is a good course if you want to learn about the basic concepts of time series

par Jonathan

•Aug 02, 2019

This is a good class. Well composed, and covers the material in a reasonable manner. Overall the best points of the class were Professor Thistleton's readings, which were very well put together and did a nice job of developing concepts. If you push yourself to follow along with them you'll develop a very sound conceptual basis about the material. Likewise, Prof. Sadigov's exercises with his notebooks were also useful, but his notes not so much.

If the class has some weak spots it's that, like a lot of classes on Coursera, the amount of time you have to spend to pass the class can be quite small if you just want to cruise and finish the course. Also, even though it's called "Practical Time Series Analysis", the majority of the material is quite conceptual. This class is a lightweight attempt to expose you to the material you'd cover if you studied it in college, but it does not just dig in and show you how to start hacking away at models. You'll need to practice a lot more on your own to develop yourself as a practitioner.

par Kazantzidis C

•Aug 23, 2019

It was the first time I deal theoritacally and practically with Time Series. It is a perfect course for a beginer.

In my opinion this course need some prerequisites in Calculus but even if one doesn't have he can complete ther course. In addition this will be a stimulus to build the adequeate mathematical backround.

Finally I would like to refer that just completing the course doesn't mean that you have aquire the pertinent knowledge. On the contrary you have to do a lot of on the job practice with reference the material of this course.

The real data for practice is the date every one finds in his occupation e.g sales , production and the like.

To the Proffesors of this you I would to refer that in the update of this course it would be very good to include a Week with Regression with time series and some theorhy and practice of detrending.

Thank Indeed.

par Luca B

•Feb 21, 2020

Very well explained! Both instructors are very competent and are complementary in their way of explaining and simplifying complex concepts. It took me five days to complete this course and I really had fun going through the theoretical details behind widely used techniques such as ARIMA and SARIMA, as well as understanding the applications of the techniques. The course is well structured and easy to follow. There is just enough theory to understand the basic concepts, applications being the main focus of this course. Congratulations to the instructors for this great course and thanks a lot for putting all the efforts to make it accessible to people that have not worked with time series before!

par Solomon W

•Mar 09, 2018

This is a great course that provides strong introduction to time series analysis and forecasting. I have benefited a lot from it as I took it to advance my career in data science. I have found the mathematical formulations in time series analysis very useful. I have also found the forecasting sections equally useful. All quizzes and in lecture questions were very helpful. The R coding practices are certainly helpful in learning the corresponding R libraries; they also provide template code that is useful for writing custom code for analysis.Many thanks to the instructors!

par Michael D

•Jun 14, 2018

I enjoyed the course, especially the theoretical part.Also I would wish there would more course, on Time Series Analysis at Coursera. Currently there is only one such course.In this course, I wish there would be more reference to the literature. Some points as determination of AR & MA order by looking at ACF & PACF plots is not clear enough to me. As I understand there is some rule of thumbs but deeper explanations are missing to me (i hope, that they exists).Anyway in my opinion is the best course in Time Series Analysis, that I ever had.

par Jeeva V

•Jul 04, 2018

The first three weeks it is hard to understand as the course content was not properly organized. some chapters and quizzes are jumbled without order. It has a lot of theory as well. But then after understanding the basics, the theoretical concepts, it is easy to follow. It gave very confident and in fact already started applying in my real world time series problems to model and forecast for future time period. Great course and would recommend to friends who are serious to learn about practical time series analysis.

par HEF

•Apr 28, 2019

The course structure is well organized from basic statistics to more advanced materials. I used to hate reading but in this course I found the reading materials quite pleasant and interesting. Only light coding involved so I guess people without coding experience would find it friendly as well. Both theoretical and applied aspects were discussed in details, and I got to know many valuable sources of finding interesting time series datasets. In summary, a really great course one must take a try!

par Ramachandra R K

•Nov 08, 2018

Decent course with a right balance between math, coding and high level explanation. AR, MA and ARMA (ARIMA) models are very well explained. I am not a big fan of R (even after this course) but it seems its time series analysis libraries and datasets are comprehensive. The best part of the course is the in-course coding examples and tasks. They really help you get hands-on into analysing various time series objects. A little more emphasis should have been made on forecasting.

par Yakup T K

•May 18, 2020

A good lesson to cover what under the hood of additive models like ARIMA and Holt-Winters Exponential Smoothing etc. It gives you the idea of what to approach time series, mathematical internals and some basic proofs. It helps you a lot to decompose a time serie in terms of seasonality, trend, autoregressive and moving average process components after making some statistical tests. It helped me to use it for my job to forecast some time series in a more accurate way.

par Sai R

•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 Chunhui G

•Jul 15, 2019

The course is very good for an introduction to time series. Few drawbacks are listed.

1. The theory behind double and triple exponential forecast are not given in the materials.

2. Some datasets are not available anymore in the Datamarket website anymore, needed to be fixed.

3. The forecast module in week 6 is kind of wired. Need more lecture to talk about what's the difference between smooth forecast and SARIMA model prediction.

par Manish k

•Aug 30, 2018

The course structure is really nice and focused on hand's on application of Time series analysis. I was able to understand the maths also quite well, thanks to the Tutor for such a simplified explanation. I would look forward to see some more advance Time Series Courses like this.

I would highly recommend this course to all the active learners willing to learn Time Series Analysis.

- L'IA pour tous
- Introduction à TensorFlow
- Réseau de neurones et deep learning
- Algorithmes, Partie 1
- Algorithmes, Partie 2
- Apprentissage automatique
- Apprentissage automatique avec Python
- Apprentissage automatique à l'aide de SAS Viya
- La programmation en R
- Intro à la programmation avec Matlab
- Analyse des données avec Python
- Principes de base d'AWS : Going Cloud Native
- Bases de Google Cloud Platform
- Ingénierie de la fiabilité du site
- Parler un anglais professionnel
- La science du bien-être
- Apprendre à apprendre
- Marchés financiers
- Tests d'hypothèses dans la santé publique
- Bases du leadership au quotidien

- Deep Learning
- Le Python pour tous
- Science des données
- Science des données appliquée avec Python
- Bases de la gestion d'entreprise
- Architecture avec Google Cloud Platform
- Ingénierie des données sur Google Cloud Platform
- Excel à MySQL
- Apprentissage automatique avancé
- Mathématiques pour l'apprentissage automatique
- Voiture autonome
- Révolutions Blockchains pour l'entreprise
- Business Analytics
- Compétences Excel pour l'entreprise
- Marketing numérique
- Analyse statistique avec R pour la santé publique
- Bases de l'immunologie
- Anatomie
- Gestion de l'innovation et du design thinking
- Bases de la psychologie positive