Відео

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?

Could you please to share the course materials? thanks!

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!!