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.
You are the best! Thank you for making these videos and giving us such wonderful explanations. You deserve one of those RUclips awards if you haven't already gotten one.
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!
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
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.
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.
Excellent explanation. it helped a lot. i have watched most of the videos and it is so good in theory. i would like to see if you can put some practical examples on VAR, ARCH, GARCH, of course with the help of packages like Eviews.
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.
Your videos seem great, do you mind giving some insight into the ARIMAX/SARIMAX models, specially how the external regressors can influence your dependent variable of the series in question.
Good talk! So in this case, deep learning based models such as LSTM should also suffer from this extrapolation behavior. The more it predict into the future, the bigger error should be witnessed ?
Hi! immensely helpful was searching for your email to put in a request. please to make a few videos explaining with practical examples of research work how the var, arima, cointegrtion, garch arch models are being used. and please put up some videos on cointegration too
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 :
During muy Msc, I had one full course on financial time series... Even though they were toy examples, predictions were awful... Don't know why many grad programmes don't focus on incorporating additional predictors (within the ts domain) rather than just the lagged values themselves and their variants. For prediction purposes, I have not found ts really useful. Interesting explanation and your videos are great btw.
This is very well done! Had never heard this interpolation vs. extrapolation distinction being articulated before. One question: for multivariant time series forecasting, if we have a data volume that is large enough for RNN, how should we think of RNN under this framework? Strictly speaking, it is an ML approach so it should be interpolating, but it is also used (when conditions allow) in some forecasting tasks. What could the best way to categorize RNN? Thank you :)
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.
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.
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 soooooo good. Please don't stop making more videos. I just subscribed to your channel.
Could you explain stability in times series? I do understand that stationary times series have finite mean and variance and their covariance or correlation is not time dependent and as two points are far off, the covariance turns to 0. Is stability in TS actually different from stationary property?
If you can give some materials of the videos, it will be better. Only videos isn't friendly to review and learning, Especially when the words of videos can't be showed and a viewer isn't native English speaker.
Excellent work.................. Request to make separate and easy videos for Machine Learning, especially real-world data, energy, water, or climate change.
That makes intuitive sense. My question is aren’t the data points on the temperature-sales graph that haven’t appeared yet also “future” data points, so they also carry the uncertainty in the time component with them? So we are still extrapolating but unlike time-series data we are not extrapolating based primarily on the time-related/varying features? But only extrapolating based on features that we may assume time doesn’t have an effect on? To me it sounded like they both have components of interpolation and extrapolation? Am I misunderstanding some concepts?
Great analysis! Yes, even a non-time-series trend has interpolation and extrapolation components. The big difference though is how often we need to perform these operations for time-series vs non-time-series data. If we think about non-time-series data, the more data points we collect, the more and more likely any future prediction will be inside the range of observed data points making it usually interpolation. This is not true for time-series data since we're mainly making predictions about the future meaning we're usually doing extrapolation.
@@ritvikmath thanks for explaining! That makes sense. I guess when we say we are usually doing interpolation on non-time-series data assumes that the variables measured aren't significantly impacted by time, or maybe time impact them equally (same magnitude, same direction) and it all cancels out -- because when we are not accounting for time as a variable when we're doing non-time-series data, the effect of time is implicit/contained in the data/observation, and we are assuming it has no effect on the (placement of the) data, when we are doing interpolation. And we know for sure for time-series data, we are definitely extrapolating because it is always beyond the data points we have because we are mapping things exactly against time. Maybe I am reading too much into this!
Hi, Can u pls share the sequence in which we should watch these videos? It's very confusing otherwise becz we seem to be jumping between the topics in unexpected manner.
Thank god we are not yet travelling back in time, like the move ' The Adam Project'. Otherwise the time series would become an interpolation.. lol !!! Awesome video by the way
Question: If we are forecasting tomorrow's ice cream sales using temperature as the only input, how are we not extrapolating? What's your exact definition of extrapolation here?
Our project was given 5 years of annual data and we're asked to predict up to 10 years. Seeing that last part now makes me scared on why the project was approved at all.
Thank you so much! Great video for me who has no math and data science knowledge. I finally got clear about the differences of regression and time series!
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! ;)
![[Limbus Company - Teaser] Season5 - Oblivion](http://i.ytimg.com/vi/8anKuEeZLeI/mqdefault.jpg)
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.
Agreed!!
You are the best! Thank you for making these videos and giving us such wonderful explanations. You deserve one of those RUclips awards if you haven't already gotten one.
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!
This is why I don't go back to school for MSc. Dude so many free sublime materials on the internet!
Same here for MSc in Finance))
Absolutely agree
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
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.
Thank you sir for this amazing content 👏 🙏
i fell in love with time-series just because of this video ;-)
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.
undoubtedly the best TS playlist on youtube that I'm aware of. Would love to see a lot more videos on this topic soon. Thanks for your hard work!
U r the Best .It's so important for me to trade in unpredictable market.
Excellent explanation. it helped a lot. i have watched most of the videos and it is so good in theory. i would like to see if you can put some practical examples on VAR, ARCH, GARCH, of course with the help of packages like Eviews.
Very useful. I checked your video for my PhD research paper. Thank you
All the best!
I appreciate all your effort my man! Thank you so much
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.
Your videos seem great, do you mind giving some insight into the ARIMAX/SARIMAX models, specially how the external regressors can influence your dependent variable of the series in question.
What clarity! Thanks so much !
Fantastic explanation
Your videos really helped me understanding a lot more. Thank you so much for that!
I have a request: Can you do VECM?
Great suggestion!
Great explanation
Good talk! So in this case, deep learning based models such as LSTM should also suffer from this extrapolation behavior. The more it predict into the future, the bigger error should be witnessed ?
your videos are so helpful and excellent, thank u for sharing. looking forward to watching more time series related videos~~
Glad you like them!
This is quite insightful. Thanks ! Could you please also talk about direct vs recursive forecasting. I am super confused about the topic.
Hi Ritvik! Thank you so much for this amazing content. Can you please do a video on ARIMAX and SARIMAX concepts
Is there a way to combine interpolation and extrapolation? Why cant we include say, predicted temperature data along with the time series predictions?
loved it !! 🤍
Brilliant. Thank you
Love your channel, thank you!!
A question please, are these videos helpful for biological time series analysis?
Hi Ritivik,
Can you suggest some books on time series so that we can follow. Thanks.
Thank you so much
Very interesting ! Thanks
very inspiring video !!! thanks
3 days left to the exam....with help of your playlist, passing seems possible
good luck!
Nice vids mate. Please, could you try Kalman Filter ala Hamilton 1995?
Thanks for the video!
Hi! immensely helpful was searching for your email to put in a request. please to make a few videos explaining with practical examples of research work how the var, arima, cointegrtion, garch arch models are being used. and please put up some videos on cointegration too
legend
Hoping to add VEC model
man keep it up
amazing!!!
Danke!
the channel has too few subscribers.
"we usually only care about predicting the future" love that quote
To be fair, imputing missing data is basically predicting the past
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 :
You put up some really useful content, like this, I appreciate it! Your channel has a great potential, it just needs some marketing ;)
During muy Msc, I had one full course on financial time series... Even though they were toy examples, predictions were awful... Don't know why many grad programmes don't focus on incorporating additional predictors (within the ts domain) rather than just the lagged values themselves and their variants. For prediction purposes, I have not found ts really useful. Interesting explanation and your videos are great btw.
This is very well done! Had never heard this interpolation vs. extrapolation distinction being articulated before. One question: for multivariant time series forecasting, if we have a data volume that is large enough for RNN, how should we think of RNN under this framework? Strictly speaking, it is an ML approach so it should be interpolating, but it is also used (when conditions allow) in some forecasting tasks. What could the best way to categorize RNN? Thank you :)
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.
This is a really great series. Thanks for uploading this.
The best explanation I ever heard about why time series are so fundamentally different! Thanks Ritvik!
Excellent explanation. I just came accross your channel today. I must say you're doing really great work!! 👍🏻✌🏻
Thanks for the clear explanations!
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.
Your channel is an "$84K" wealth of knowledge.
I am learning more by watching your videos than I did in my graduate program. Well done!
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 soooooo good. Please don't stop making more videos. I just subscribed to your channel.
Could you explain stability in times series? I do understand that stationary times series have finite mean and variance and their covariance or correlation is not time dependent and as two points are far off, the covariance turns to 0. Is stability in TS actually different from stationary property?
If you can give some materials of the videos, it will be better. Only videos isn't friendly to review and learning, Especially when the words of videos can't be showed and a viewer isn't native English speaker.
Do the videos in this playlist follow a logical order for the arguments?
Great explanation! Your videos are really awesome! Keep posting videos
Thanks! Appreciated
hey , how can we connect with you?
what are some books you suggest for time series
Great video I really appreciate the work you do!
Thank you! Is there a way to combine both temperature and time? Would that be vector autoregression?
Yes...Y_t = [T_t, C_t]'
Excellent work.................. Request to make separate and easy videos for Machine Learning, especially real-world data, energy, water, or climate change.
Super helpful!
That makes intuitive sense. My question is aren’t the data points on the temperature-sales graph that haven’t appeared yet also “future” data points, so they also carry the uncertainty in the time component with them? So we are still extrapolating but unlike time-series data we are not extrapolating based primarily on the time-related/varying features? But only extrapolating based on features that we may assume time doesn’t have an effect on? To me it sounded like they both have components of interpolation and extrapolation? Am I misunderstanding some concepts?
Great analysis!
Yes, even a non-time-series trend has interpolation and extrapolation components. The big difference though is how often we need to perform these operations for time-series vs non-time-series data.
If we think about non-time-series data, the more data points we collect, the more and more likely any future prediction will be inside the range of observed data points making it usually interpolation.
This is not true for time-series data since we're mainly making predictions about the future meaning we're usually doing extrapolation.
@@ritvikmath thanks for explaining! That makes sense. I guess when we say we are usually doing interpolation on non-time-series data assumes that the variables measured aren't significantly impacted by time, or maybe time impact them equally (same magnitude, same direction) and it all cancels out -- because when we are not accounting for time as a variable when we're doing non-time-series data, the effect of time is implicit/contained in the data/observation, and we are assuming it has no effect on the (placement of the) data, when we are doing interpolation. And we know for sure for time-series data, we are definitely extrapolating because it is always beyond the data points we have because we are mapping things exactly against time.
Maybe I am reading too much into this!
are you a quant?
This is amazing
thank you!
Hi,
Can u pls share the sequence in which we should watch these videos? It's very confusing otherwise becz we seem to be jumping between the topics in unexpected manner.
Thank god we are not yet travelling back in time, like the move ' The Adam Project'. Otherwise the time series would become an interpolation.. lol !!! Awesome video by the way
Question: If we are forecasting tomorrow's ice cream sales using temperature as the only input, how are we not extrapolating? What's your exact definition of extrapolation here?
Thank you so much for making these videos! I like how you use examples to illustrate the concepts in time series, which is very easy to understand.
Thorough and great explanation of this subject matter. Thanks so much
Our project was given 5 years of annual data and we're asked to predict up to 10 years. Seeing that last part now makes me scared on why the project was approved at all.
Shit - finally i got the point about an AR(1) Model
awesome!
Thank you so much! Great video for me who has no math and data science knowledge. I finally got clear about the differences of regression and time series!
tx sir
loved your way of presentation... nice explanation... keep going...
I just stumbled on to your channel. Great stuff mate.
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! ;)
Excellent presentation on diff between regression and time series
Thanks Ritvik
Rajavel KS
Bengaluru
Thats a great explanation! Can you share some reading resources also?
I was here before this channel got 1 million subscribers.
Thanks. Learned a lot. Great explanation.
Your classes are excellent. Thanks for sharing.
what if we need to generate some missing data from the past using time series ?
You should be teaching at university!
tnx for this video, very insightful
Such a nice video to kick off the series!
Really good video. Loved the theory.
Wow, you are the best, man!
Awesome video! Thanks!
very very well explained keep up
Thanks a lot!!!
Great video !
you are very good at teaching, thanks for this videos
You are welcome!
Your explanations are great! Clear and simple.