![Lasse Engbo Christiansen](/img/default-banner.jpg)
- 132
- 365 417
Lasse Engbo Christiansen
Приєднався 29 жов 2011
COVID-19 Webinar on model-based evaluation of scenarios for reopening Denmark
Models to simulate the spread of SARS-CoV-2 (the virus causing COVID-19) have been developed by researchers from Statistics Denmark, the Technical University of Denmark, the University of Copenhagen and Roskilde University. These models have been used to evaluate the predicted burden on critical hospital functions, including beds on normal hospital wards and intensive care units, in connection with the gradual reopening of Denmark. The webinar will focus on the technical elements of the models, including descriptions of the models and parameter estimates that were used.
The webinary was moderated by Jakob Stoustrup (Professor, Department of Electronic Systems, Aalborg University. Following the presentations, there were a discussion of the models and parameters by Susanne Ditlevsen (professor, Department of Mathematical Sciences, University of Copenhagen), Mogens Fosgerau (professor, Department of Economics, University of Copenhagen) and Jens Lundgren (professor, Department of Infectious Diseases at Rigshospitalet and University of Copenhagen).
The group consists of:
Robert Leo Skov, SSI, project lead (Presenter 1)
Kaare Græsbøll, DTU Compute (Presenter 2 & 5)
Theis Lange, KU SUND (Presenter 3)
Lasse Engbo Christiansen, DTU Compute (Presenter 4)
Laust Hvas Mortensen, Danmarks Statistik
Sune Lehmann, DTU Compute
Uffe Høgsbro Thygesen, DTU Compute
Jonas Lybker Juul, DTU Compute
Carsten Thure Kirkeby, KU SUND
Matt Denwood, KU SUND
Tariq Halasa, KU SUND
Kim Sneppen, KU NBI
Anders Perner, Region H
Lone Simonsen, RUC
Viggo Andreasen, RUC
The webinary was moderated by Jakob Stoustrup (Professor, Department of Electronic Systems, Aalborg University. Following the presentations, there were a discussion of the models and parameters by Susanne Ditlevsen (professor, Department of Mathematical Sciences, University of Copenhagen), Mogens Fosgerau (professor, Department of Economics, University of Copenhagen) and Jens Lundgren (professor, Department of Infectious Diseases at Rigshospitalet and University of Copenhagen).
The group consists of:
Robert Leo Skov, SSI, project lead (Presenter 1)
Kaare Græsbøll, DTU Compute (Presenter 2 & 5)
Theis Lange, KU SUND (Presenter 3)
Lasse Engbo Christiansen, DTU Compute (Presenter 4)
Laust Hvas Mortensen, Danmarks Statistik
Sune Lehmann, DTU Compute
Uffe Høgsbro Thygesen, DTU Compute
Jonas Lybker Juul, DTU Compute
Carsten Thure Kirkeby, KU SUND
Matt Denwood, KU SUND
Tariq Halasa, KU SUND
Kim Sneppen, KU NBI
Anders Perner, Region H
Lone Simonsen, RUC
Viggo Andreasen, RUC
Переглядів: 1 579
Відео
02417 Lecture 12 part A: ARMA models on State space form
Переглядів 7 тис.6 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 9 part D : VARMA(p,q) as VAR(1) model
Переглядів 3,9 тис.6 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 9 part E: Identification and estimation of multivariate models
Переглядів 1,3 тис.6 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 12 part F: Kalman filter with time varying coefficients
Переглядів 3,8 тис.6 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 12 part E: ACF with missing data
Переглядів 8536 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 12 part C: Example: Initialization of Kalman filter
Переглядів 5 тис.6 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 12 part D: Maximum Likelihood with Kalman filter
Переглядів 4,4 тис.6 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 12 part B: Example: Random walk with observation noise
Переглядів 1,9 тис.6 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 13 part F: Outlook to more advanced topics: Nonlinear models
Переглядів 7446 років тому
This is part of the course 02417 Time Series Analysis as it was given in the fall of 2017 and spring 2018. The full playlist is here: ua-cam.com/play/PLtiTxpFJ4k6TZ0g496fVcQpt_-XJRNkbi.html You can download the slides here: drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6?usp=sharing The course is based on the book: Time Series Analysis by Henrik Madsen: henrikmadsen.org/books/t...
02417 Lecture 13 part E: KF for parameters
Переглядів 6076 років тому
02417 Lecture 13 part E: KF for parameters
02417 Lecture 13 part C: Example: RLS with forgetting
Переглядів 8976 років тому
02417 Lecture 13 part C: Example: RLS with forgetting
02417 Lecture 13 part B: RLS with forgetting
Переглядів 2,6 тис.6 років тому
02417 Lecture 13 part B: RLS with forgetting
02417 Lecture 12 part G: AR(1) with observation noise
Переглядів 9686 років тому
02417 Lecture 12 part G: AR(1) with observation noise
02417 Lecture 6 addon - ACF and PACF
Переглядів 7 тис.6 років тому
02417 Lecture 6 addon - ACF and PACF
02417 Lecture 4 part C: Local trend model
Переглядів 1,7 тис.6 років тому
02417 Lecture 4 part C: Local trend model
02417 Lecture 4 part D: Variance in local trend models
Переглядів 1,5 тис.6 років тому
02417 Lecture 4 part D: Variance in local trend models
02417 Lecture 4 part B: Choosing lambda in exponential smoothing
Переглядів 1,5 тис.6 років тому
02417 Lecture 4 part B: Choosing lambda in exponential smoothing
02417 Lecture 4 part A: Exponential smoothing
Переглядів 2,1 тис.6 років тому
02417 Lecture 4 part A: Exponential smoothing
02417 Lecture 4 part E: Variance in local trend models - simulation example
Переглядів 1,5 тис.6 років тому
02417 Lecture 4 part E: Variance in local trend models - simulation example
02417 Lecture 3 part B: Estimating in global trend models
Переглядів 1,7 тис.6 років тому
02417 Lecture 3 part B: Estimating in global trend models
02417 Lecture 2 part D: Predicting in linear models
Переглядів 2,1 тис.6 років тому
02417 Lecture 2 part D: Predicting in linear models
02417 Lecture 2 part A: Ordinary least squares in linear model
Переглядів 5 тис.6 років тому
02417 Lecture 2 part A: Ordinary least squares in linear model
02417 Lecture 2 part C: Weighted least squares
Переглядів 2,9 тис.6 років тому
02417 Lecture 2 part C: Weighted least squares
02417 Lecture 3 part A: Global trend models
Переглядів 2,5 тис.6 років тому
02417 Lecture 3 part A: Global trend models
02417 Lecture 3 part D: R example on regression
Переглядів 1,7 тис.6 років тому
02417 Lecture 3 part D: R example on regression
02417 Lecture 3 part C: Global trend model - example
Переглядів 1,5 тис.6 років тому
02417 Lecture 3 part C: Global trend model - example
6:57 prediction interval
6:49
thanks so much! straight to the point!
good info for people who are crash coursing this
Good presentation 👏
Great video sir
Thank you a lot. The video and your examples are clear and easy to understand :)
is he just going to give complex mathematical equations the whole time and not explain what any of the symbols mean or what the equation is supposed to represent? I just watched a 10 minute explanation on marginal density function and have no clue what it is
0:31
where can I find the textbook?
🎯 Key Takeaways for quick navigation: 00:00 🤖 *Introduction to course 02417 Time Series Analysis in the fall 2018 semester at DTU* - Introduction to flipped classroom format with video lectures and weekly discussion sessions - Overview of course structure, assignments, and expectations 07:00 📈 *Motivation for time series analysis* - Examples of time series data like fur trade records, stock prices, airline passengers - Concepts like stationarity, dynamical systems, prediction intervals 15:00 👩🏫 *About the instructor* - Background and contact information for the lecturer - Office location and hours for student questions 20:00 🎯 *Course objectives* - Characterizing time series data, modeling, prediction, uncertainty quantification - Linear systems theory, input-output models, statistical concepts 27:00 💡 *Example relating heating system to dynamical systems* - Heating system state depends on past states like temperature - Violates assumption of independent observations in linear regression 36:00 📊 *Student background in statistics and probability* - Assessing prior knowledge to tailor lecture content - multivariate distributions and linear modeling concepts 45:00 📝 *Defining multivariate normal distribution* - Generalization of univariate normal distribution - Mean vector, covariance matrix, density function 55:00 🍊 *Marginal and conditional densities* - Integrating joint density over unwanted dimensions - Relation to independence of variables Made with HARPA AI
Thank you so much for the video. The examples helped me understand the concept much better! I have a question, though. In 07:53, there is one significant PACF, that's why you consider it as AR(1) process. However, that PACF is in lag 5. Why is it not AR(5)?
Godsend .
Best time series lesson ever! (Whole lecture series is GREAT..)
Sir, Thank you! Very much ARIMA Model Identification aspects and issues are expressed more clear.
Graet explanation. I need the PPT/DOC too.
All ACF and PACF interpretation videos show very clean graphs that are easy to interpret, so its never clear how to interpret ones that are not so standard. This video explained so easily how to do it. I can't believe this is the only one I have come across in all my searching that does this.
When does the time series content actually start?
Part B was randomly cut off somehow
It helped! Thank you for giving examples from all the possible scenarios.
Hi, Prof. Lasse, I am Miss Fei Li. I am a first-year PhD Finance student. I come from UK. I am listening your time-series analysis in R course. Is it possible if you share the access of the data? I hope to try to type code with the videos' guidance. Appreciate!
Thanks
No. Take the mic away from your mouth.
Hey, @4:52, shouldnt the number of estimated parameters be m instead of p, from the notations mentioned at the start of the lecture.
Damn, why is it always youtube that explains the topics better than my professors
what if the trend is not as easy as these? what if there's no exponential decay on both plots?
The examples really helped ❤️
Phenomenal video! I've spent half a semester on ARIMA in my business forecasting methods class for my Masters program and I didn't understand what was going on until I watched this video. You are a great teacher. Thank you for the content!
The best explanation out on UA-cam on this subject till date!
drive.google.com/drive/folders/1OYamq8_PONteNHEdgkEG-jLvraeaGOp6
For anyone wondering, he's teaching from "Time Series Analysis " by Henrik Madsen. Henrik has a website with exercises and solutions for that book.
Awesome explanation!
Thank you!
How could I choose seasonal order? I have daily data with period of about 365 days. Shall I take m = 365?
Thank you for sharing your courses, they are very clear from theory to concrete examples.
Thank you for wonderful lectures. If I will be a guru as well, then I will contibute to the world like you!! l respect you profecssor 💕💕💕 😊😊
Some feedback in order to improve things: 1. Either there should be a big whiteboard or a small one, right now it is hard to see what's on both. 2. The small board, if needs to be there, should be placed on the lhs, so the lecturer does not cover the board with his right arm. 3. Since there are slides, why write stuff manually? Thanks for your content, but it is hard to follow :( Merry Xmas!
8:46 was though to guess :)
Thank you very much for this generous lecture -from Ethiopia.
Could you please tell me what book are you using?
He's teaching from "Time Series Analysis " by Henrik Madsen. Henrik has a website with exercises and solutions for that book.
Could you please to share the course materials? thanks!
You can see some video descriptions when you open the playlist instead of individual videos. There are a couple of links, and one of them is the google link for the slides of this lecture.
He's teaching from "Time Series Analysis " by Henrik Madsen. Henrik has a website with exercises and solutions for that book.
Thanks for the lectures! Very useful :)
Please explain how do i use R program to get the predict Sir...
Very good and clear lectures! Thank you for your efforts!
You are an amazing professor! Thank you!
Thank you for the video and may I know if the PACF and ACF plots at the begining of the video are for order p and q or only for order 1?
good explanation, many thanks.
Very useful information compress in just 13 minutes, thanks a lot! . Btw I lived 1 year in Odense back in 2018, if I'm not wrong you have Fyn accent right? I didn't expect to listen that accent searching tutorials for grid searching method haha. Greetings from Chile :)
Not easy to understand what are you saying !! Take your time and explain
Thank you for explaining these concepts so clearly!!