18:46 State Space Models 25:50 Structural Time Series 30:46 Kalman Filter 38:40 Implementing Structural Time Series 1:04:00 Hidden Markov Models (HMMs) 1:11:00 Baum-Welch and Viterbi Algorithms 1:20:08 Implementing Gaussian HMM 1:40:00 Machiene Learning for Time Series 1:57:40 Implementation ML for TS 2:44:19 Deep Learning for Time Series 2:47:20 Recurrent Neural Networks (RNNs) 2:51:33 Convolutional Neural Networks (CNNs) 2:56:35 Implementing Deep Learning
Ahhh such a polite teacher and the way she talk abd explain. OMG she and people like her are really a gift to our society. Stay safe, keep teaching and keep smiling. thank you
Have to agree with everyone on here. Excellent lecture - a great mix of detail and higher-level overview. It sounds like this isn't even her full-time gig. Impressive. My new learning strategy - watch every one of her you-tube tutorials.
NOTE: 1:03:36 MAE calculation should be a subtraction of the fitted and the training data, not a concatenation with a comma! Ends up being like 0.072191.....
Wow..took sometime to complete it...but this is best explanation for time series so far..although it tells me to learn more about these things ....one should be very much familiar with the numpy to code these things
Hello, everybody I type gcag_mod=sm.tsa.UnobservedComponents(train['GCAG'], **model) gcag_res=gcag_mod.fit() then I got name 'train' is not defined. Could anybody help me?
At 1:03:21 when Aileen speaks about the mean absolute error, the code in Cell 47 is wrong: instead of a negative sign, there is a comma, and this is still present in the github repo as of this writing.
@@joaoantonio9337 Hi, you can read it behind her on the white board, but she also mentions it at 37:42. More specifically, here it is: github.com/theJollySin/scipy_con_2019/tree/master/modern_time_series_analysis
Great talk. What is being forecasted at 2':15" using XGBoost? It seems like she is not using the time series values at all for regression. What is the target value in the training?
This is an amazing presentation on many levels! Perhaps a very odd question, but would anyone be able to explain how to establish a presentation setup as shown here with the the speaker on camera and the code window in full display?
I'm using RNN for my PG thesis work. I've a query. Do we have to run stationarity test for our time series data before feeding it in the neural network model... or this step is only required in traditional time series models like ARIMA?
I am a little confused about the feature generation in the ML forecasting part. It seems like we're spending a lot of effort to create features that end up not being very predictive of the target. Couldn't we use use the lag values themselves as features in the model? Xgboost (or even a simple linear regression) should be able to detect the correlations and provide a decent prediction.
Great video with a lot of depth. Knowledgeable speaker. Thanks for creating and sharing. I have a couple of "concerns": * Why does none of the data have measurement units? It is almost as if statisticians do not care what they analyze (it is just numbers). Look at the charts, there is no measurement unit on the x-axis (looks like it is mostly months) and no measurement unit on the Y-axis (what does it mean that the global temperature is varying between -0.75 and 1.25 but what is it? Apples? Oranges? Degree F? Degree C? Kelvin? Is it absolute? Is it a delta from a base measurement (relative)? Where was the measurements taken? I am concerned about this, as a measurement unit is one of the most basic contextualization elements. My middle school math teacher would mark answers as wrong if the measurement unit was missing. * My not so humble opinion is that dynamic time warping is bullshit. There are so many issues with the approach. The presenter is taking two sinewaves and merging them together to get a correlation and then use the result to show that there is correlation. This is the definition of circular argumentation. Another issue is that there is an assumption that the correlation is positive, what if a lower value in variable 1 caused a higher value in variable 2, then the whole error function would fail. An no point does the presenter show the warped result. This completely messes with the notion that time series generally deals with ordered continous data. It would be much better to take a fourier transform and look at the harmonics of the frequencies.
very god tutorial - How to start learning time series from scratch and fourier analysis for stock market time cycles? Or any good books Or Courses to study?
Surely an LSTM (or any recurrent neural net) is an machine learning model setup/designed for time series? Also random forests do give feature importances (like XGBoost). Still enjoyed the talk :)
she did say that at the beginning of the talk - but what is old may still be some of the best tools for the job today - her words again... because changes in infra, data and tools have allowed better results from long standing concepts and approaches...
I would like to know if there is a better talk/repo/book any kind of resource you can suggest. (not trying to defend the video here, I want to look into the new stuff)
a THREE HOUR lecture on Time Series Analysis. What a gift!
18:46 State Space Models
25:50 Structural Time Series
30:46 Kalman Filter
38:40 Implementing Structural Time Series
1:04:00 Hidden Markov Models (HMMs)
1:11:00 Baum-Welch and Viterbi Algorithms
1:20:08 Implementing Gaussian HMM
1:40:00 Machiene Learning for Time Series
1:57:40 Implementation ML for TS
2:44:19 Deep Learning for Time Series
2:47:20 Recurrent Neural Networks (RNNs)
2:51:33 Convolutional Neural Networks (CNNs)
2:56:35 Implementing Deep Learning
1:05:11 hidden markov model
1:40:00 machine learning for time series
2:44:00 deep learning for time series
This comment was really helpful 🙏🏼
Best time series talk i have ever watched.
Ahhh such a polite teacher and the way she talk abd explain. OMG she and people like her are really a gift to our society. Stay safe, keep teaching and keep smiling. thank you
Best explanation of time series analysis I've ever seen. Very good mix of intro to the models, examples, and links to more in-depth information.
Have to agree with everyone on here. Excellent lecture - a great mix of detail and higher-level overview. It sounds like this isn't even her full-time gig. Impressive. My new learning strategy - watch every one of her you-tube tutorials.
We think alike
Very well organized, informative, thorough, and polished. All-around impressed with Ms. Nielsen.
Very VERY good explanation of the different approaches to time series analysis. Thanks a lot!
Such an excellent video. Took me ages to finish but still, wish it was longer.
An absolute delight of tutorial. Many thanks for preparing it and communicating it so well!
This is an excellent tutorial and I like the fact that Aileen didn't skip the math part of the algorithms
NOTE: 1:03:36 MAE calculation should be a subtraction of the fitted and the training data, not a concatenation with a comma! Ends up being like 0.072191.....
should leave the link for the lecture she mentions in the description. great material
Wow..took sometime to complete it...but this is best explanation for time series so far..although it tells me to learn more about these things ....one should be very much familiar with the numpy to code these things
I dont see any slides in the mentioned website or link . can some one help me to get the link ?
Very eloquent and dominant speaker
Super cool presentation ! Thanks a lot
Hello, everybody
I type
gcag_mod=sm.tsa.UnobservedComponents(train['GCAG'], **model)
gcag_res=gcag_mod.fit()
then I got
name 'train' is not defined. Could anybody help me?
Most important video on youtube
At 1:03:21 when Aileen speaks about the mean absolute error, the code in Cell 47 is wrong: instead of a negative sign, there is a comma, and this is still present in the github repo as of this writing.
Hello Sam!
Where did you find the github?
@@joaoantonio9337 Hi, you can read it behind her on the white board, but she also mentions it at 37:42. More specifically, here it is: github.com/theJollySin/scipy_con_2019/tree/master/modern_time_series_analysis
@@samm9840 thank you!
@@samm9840 Thanks....I was searching this url too
Great talk. What is being forecasted at 2':15" using XGBoost? It seems like she is not using the time series values at all for regression. What is the target value in the training?
She is outstanding!
perfect teaching, It was very informative. Thank you
Outstanding! Congratulations and thank you!
Fantastic video!
This is an amazing presentation on many levels! Perhaps a very odd question, but would anyone be able to explain how to establish a presentation setup as shown here with the the speaker on camera and the code window in full display?
What to do before applying np.log if our data has zero values? What's the best technique? I added +.000000001 to all values? is that correct?
Brilliant presentation.
Is there anything about change point detection in the lecture?
Where can i get the notebook? Can someone link me to it?
Excellent and in detail explanation.
is the ppt available for this presentation ?
Can someone help me to find the notebooks???
Truly phenomenal.
I'm using RNN for my PG thesis work. I've a query. Do we have to run stationarity test for our time series data before feeding it in the neural network model... or this step is only required in traditional time series models like ARIMA?
I am a little confused about the feature generation in the ML forecasting part. It seems like we're spending a lot of effort to create features that end up not being very predictive of the target. Couldn't we use use the lag values themselves as features in the model? Xgboost (or even a simple linear regression) should be able to detect the correlations and provide a decent prediction.
Great tutorial! Thanks!
Cogent and useful well done.
Great talk! Keep in mind that many of the things that are said to be computationally taxing are only so if one implements them in Python.
Github link - github.com/theJollySin/scipy_con_2019/blob/master/modern_time_series_analysis/README.md
37:50 The python programming starts
Coding begins again for hidden markov models at around 1:20:00
Notebook #3 at 1:58:00
how can I join the slack channel, please
if this code is already pushed on git..could you provide the github link to your code?
In case you haven't found it already, here's the link - github.com/theJollySin/scipy_con_2019/blob/master/modern_time_series_analysis/README.md
@@gagandeep4850 I struggled to get it from what was written on the white-board behind her, but then you already posted it. Thanks!
@@gagandeep4850 Thanks
@@gagandeep4850 can you send me your mail. Thank you very much
Nice talk and topic cover!
Amazing talk! Where could I fin the github?
Well done!
Great video with a lot of depth. Knowledgeable speaker.
Thanks for creating and sharing. I have a couple of "concerns":
* Why does none of the data have measurement units? It is almost as if statisticians do not care what they analyze (it is just numbers). Look at the charts, there is no measurement unit on the x-axis (looks like it is mostly months) and no measurement unit on the Y-axis (what does it mean that the global temperature is varying between -0.75 and 1.25 but what is it? Apples? Oranges? Degree F? Degree C? Kelvin? Is it absolute? Is it a delta from a base measurement (relative)? Where was the measurements taken? I am concerned about this, as a measurement unit is one of the most basic contextualization elements. My middle school math teacher would mark answers as wrong if the measurement unit was missing.
* My not so humble opinion is that dynamic time warping is bullshit. There are so many issues with the approach. The presenter is taking two sinewaves and merging them together to get a correlation and then use the result to show that there is correlation. This is the definition of circular argumentation. Another issue is that there is an assumption that the correlation is positive, what if a lower value in variable 1 caused a higher value in variable 2, then the whole error function would fail. An no point does the presenter show the warped result. This completely messes with the notion that time series generally deals with ordered continous data. It would be much better to take a fourier transform and look at the harmonics of the frequencies.
nice. any code available?
very god tutorial - How to start learning time series from scratch and fourier analysis for stock market time cycles?
Or any good books Or Courses to study?
Would recommend digital signal processing by Alan, Oppenheimer
Great talk, ty.
Surely an LSTM (or any recurrent neural net) is an machine learning model setup/designed for time series? Also random forests do give feature importances (like XGBoost).
Still enjoyed the talk :)
fbprophet too
Amazing
Really informative. Thanks a lot.
Great time series talk! Thanks, the speaker speak really fast :P
Thank you.
I'm in love
god, you are amazing
1:40:00 th-cam.com/video/v5ijNXvlC5A/w-d-xo.html - machine learning for time series
52:27
1:03
2:01:17
When you drink coffee while talking, you get slime in your throat.
Just say "states pace".
54:19 Wow, a passive-aggressive lecturer. Who doesn't like them?
Did she air quotes global warming lol. Why the air quotes hahah
You are just beautiful!
Popo
This is very old stuff...
she did say that at the beginning of the talk - but what is old may still be some of the best tools for the job today - her words again... because changes in infra, data and tools have allowed better results from long standing concepts and approaches...
I would like to know if there is a better talk/repo/book any kind of resource you can suggest. (not trying to defend the video here, I want to look into the new stuff)
the armpits
2:17:20