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

a THREE HOUR lecture on Time Series Analysis. What a gift!

1:05:11 hidden markov model 1:40:00 machine learning for time series 2:44:00 deep learning for time series

Very VERY good explanation of the different approaches to time series analysis. Thanks a lot!

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.

An absolute delight of tutorial. Many thanks for preparing it and communicating it so well!

Very well organized, informative, thorough, and polished. All-around impressed with Ms. Nielsen.

Such an excellent video. Took me ages to finish but still, wish it was longer.

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

Super cool presentation ! Thanks a lot

Best time series talk i have ever watched.

Outstanding! Congratulations and thank you!

perfect teaching, It was very informative. Thank you

Truly phenomenal.

Excellent and in detail explanation.

Great tutorial! Thanks!

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

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.

Cogent and useful well done.

Nice talk and topic cover!

should leave the link for the lecture she mentions in the description. great material

This is an excellent tutorial and I like the fact that Aileen didn't skip the math part of the algorithms

Great talk, ty.

She is outstanding!

Very eloquent and dominant speaker

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?

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.....

Well done!

Most important video on youtube

Really informative. Thanks a lot.

Thank you.

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?

Is there anything about change point detection in the lecture?

Amazing

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.

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?

I dont see any slides in the mentioned website or link . can some one help me to get the link ?

Great time series talk! Thanks, the speaker speak really fast :P

37:50 The python programming starts

Github link - github.com/theJollySin/scipy_con_2019/blob/master/modern_time_series_analysis/README.md

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.

Amazing talk! Where could I fin the github?

I'm in love

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?

god, you are amazing

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.

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?

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?

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.

Where can i get the notebook? Can someone link me to it?

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 :)

is the ppt available for this presentation ?

Can someone help me to find the notebooks???

if this code is already pushed on git..could you provide the github link to your code?

how can I join the slack channel, please

52:27

When you drink coffee while talking, you get slime in your throat.

1:40:00 kzfaq.info/get/bejne/rJuZnbGJ29KnZXU.html - machine learning for time series

1:03

nice. any code available?

2:17:20

You are just beautiful!

Did she air quotes global warming lol. Why the air quotes hahah

Popo

This is very old stuff...

the armpits

2:01:17