TSA Lecture 1: Noise Processes

[@junechu9701]

This series is awesome! So grateful for your work!

[@Eizengoldt]

Professor Kashlak thank you so much for this course! I’m graduating soon and was worried because I missed out on the Time Series class at NYU due to our course rotation, but you’ve really helped me !

[@dongli7157]

Those are excellent learning courses. I have a project which requires me to use time series to predict the demand. This course right fits my needs. Thanks a lot

[@adamkashlak7501]

I'm glad it's helpful. Making these takes a lot of work, so I'm glad their use extends beyond my local students.

[@sowmyaraoch]

Awesome lecture! Very clear. Could you please do a playlist for statistics needed for data science

[@yadavindu]

You have really an interesting way of explaining things, thanks a lot professor...

[@adamkashlak7501]

Thanks for the comment. I may have to continue posting lectures post-pandemic if people like them.

[@yadavindu]

@@adamkashlak7501 It would very kind of you to do so, please keep doing it.

[@fritzsierratintaya4911]

It is the best time series course I have ever bought. Thank you very much for sharing your knowledge. We will support your channel. Please could you share the class notes. Thank you

[@adamkashlak7501]

I'm glad you like it. I need to add links in the video description, but for now, my typed notes are at sites.ualberta.ca/~kashlak/kashTeaching.html.

[@eh2028]

​@@adamkashlak7501Hi professor, by any chance do you also have your written notes from this video?

[@mohamedtarek8899]

Thanks Adam for your lectures, i have a question, what i understand is that our time indexed data (time series) which is based on a stochastic variables can be seen as or processed as a noise in this lecture you assumed it's a white noise what has a specified features like mean 0 and const variance, also with multi var Gaussian the same it's a predefined or pre assumed distribution for the data which is kind of a way for statistician to deal with data in simple way, so how can i decide if data and it's fluctuations follow this kind of pattern i mean white noise, and am i understand the concept well? thanks

[@vikdfr]

Great storytelling! Thank you, Prof.

[@yleswill3053]

The lectures are really helpful, Thanks !!

[@adamkashlak7501]

I'm glad they are helpful for you. Let me know if you have any questions.

[@M8s08BgT91vAAAAA]

Amazing stuff, thank you so much!

[@reanwithkimleng]

Thanks sir❤❤❤❤

[@martinsanchez-hw4fi]

In 22:58 you say it is a random walk with drift, but wouldnt it be (as you wrote it) an AR 1? I think a drift would be an additive term dependent on time only, right? X_t ++at+w_t

[@cremildamondlane3900]

hi, professor Adam i did math stats, anova and regression end of last year, this year i did stochastic and im currently doing time series. I want to deepen my stats knowlege any sugestions on which courses i should do next?

[@EthanChang-m3t]

Hey Professor, I'm a student at UC Davis taking sta 137 (Time Series Analysis) and we're doing like I'm not even sure what but deriving gamma for ACF and ACVF and psi for some functions and I'm super confused. Could you help explain what those variables mean by any chance? I decided I'm going to watch your videos in addition to my professor's lectures!

[@mohammadrezarazavian9305]

this is great!

[@thirdreplicator]

Adam Kashlak for prime minister!

[@adamkashlak7501]

Thank you for your support. Maybe 20 years from now, I'll try my hand at politics.

[@amorphous8826]

👍

[@mukulkumar2316]

Hi prof, could you please upload your computing course( one desling with monte carlo estimation) here? I don't know if it us too much to ask , but could you also upload some hw exercises based on the content on yhis channel? Thanks for this public channel

[@mjchannel506]

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[@JohnChu-z4w]

Very good teaching videos, 2024.2.1