Why Are Time Series Special? : Time Series Talk

So ... what's so special about time series?

[musclesmalone]

Dude, you are carrying me through my data science MSc. Sincere gratitude for all of your sublime teaching resources. If it was common practice for schools and universities to train their staff to teach science and mathematics the way you teach it, quite literally the world would be a better place!

[HussainShamsu]

Thank you sir for this amazing content 👏 🙏

[lennyn]

"we usually only care about predicting the future" love that quote

[aakuthotaharibabu]

almost wasted so many months to find this master piece tutorials for time series. Instead of telling its difficult / complex you gave a clear idea why its important to learn timeseries. Thank you for the complete playlist.

[alexei.domorev]

The best explanation I ever heard about why time series are so fundamentally different! Thanks Ritvik!

[ladieimp]

You are the best! Thank you for making these videos and giving us such wonderful explanations. You deserve one of those YouTube awards if you haven't already gotten one.

[avananana]

It's actually nuts how nonsensical our lecturer is trying to explain all these concepts. Been binging this channel for a bit over the past few days and everything makes so much more sense now. It's crazy how bad some people are at teaching, yet take on the job of teaching. Obviously it's nothing personal, but my god if you're gonna apply as a teacher, lecturer, whatever, at least have some basics in teaching :<

[ib-od]

Notes for future
- TS is an extrapolation problem and error keeps on increasing as we move away from known data
- Reg is an interpolation problem and error is more or less same, since prediction is usually made in the range of available data.

[upendrap]

Thanks to bard AI, it provided a link to your website which led me to watch your videos. Your teaching is not only has depth but also easy to understand. Such a rare combination. Please keep doing this and thank you.

[dasundesilva]

Holy shit dude, I've been studying simulating paths for stocks and stuff for the past month and I've been struggling to understand the point. The prediction intervals and accumulating uncertainty explanation clears up so much! I am almost in tears! thank you

[abhishekjn]

i fell in love with time-series just because of this video ;-)

[EdgarPE]

You put up some really useful content, like this, I appreciate it! Your channel has a great potential, it just needs some marketing ;)

[cyzfozi]

It's crucial to be clear about the terminology, as the terms "regression" and "interpolation" have specific meanings and uses in statistical and mathematical contexts. If someone refers to regression as a form of interpolation, they may be emphasizing the predictive aspect of regression models within the observed data range. However, it's not a universally accepted terminology in formal statistical discussions.

[xXxIMMORTALxXx]

Just watched your VAR video and this is the second video of yours I've watched. I gave you a like when I was just 7 secs in. This does not disappoint. Your explanation is good. Please don't stop making more videos. I just subscribed to your channel.

[tomtrask_YT]

Your prediction interval on the demand vs temperature graph also grew at the highs and lows for exactly the same reason - your basis for the extreme temperature demands is based on less data than near the average, say, monthly temperatures where you have tons of data.

[hameddadgour]

I am learning more by watching your videos than I did in my graduate program. Well done!

[BetoAlvesRocha]

What an amazing way to introduce Time Series, mate! Can't wait to see the other videos in your playlist about this subject.

Many thanks for that and greetings from Brazil! ;)

[ranitdey]

Excellent explanation. I just came accross your channel today. I must say you're doing really great work!! 👍🏻✌🏻

[rahulmandalsky]

The Non-time series way where we find out the relation between x_i and y, the value can be interplated or extrapolated based on x_i. It need not be interpolation always. Say we have linear regression y=3x+2. We can find y for any points outside of what values we have. So it can be interpolation or extrapolation. But for time series X_i which is time now will never occur again so it will always be extrapolation.

[MrCEO-jwvm]

Thorough and great explanation of this subject matter. Thanks so much