Modern Time Series Analysis | SciPy 2019 Tutorial | Aileen Nielsen 

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

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

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.

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

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

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

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

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

Best time series talk i have ever watched.

Super cool presentation ! Thanks a lot

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.

perfect teaching, It was very informative. Thank you

Truly phenomenal.

Outstanding! Congratulations and thank you!

Great tutorial! Thanks!

Excellent and in detail explanation.

Cogent and useful well done.

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

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

Nice talk and topic cover!

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

Very eloquent and dominant speaker

Great talk, ty.

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?

She is outstanding!

Well done!

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.....

Most important video on youtube

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?

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?

Really informative. Thanks a lot.

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

Amazing

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.

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?

Thank you.

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.

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?

Amazing talk! Where could I fin the github?

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

37:50 The python programming starts

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.

I'm in love

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

god, you are amazing

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?

is the ppt available for this presentation ?

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.

Can someone help me to find the notebooks???

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 :)

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

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?

nice. any code available?

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

2:17:20

52:27

1:03

1:40:00 ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-v5ijNXvlC5A.html - machine learning for time series

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

You are just beautiful!

Popo

This is very old stuff...

the armpits

2:01:17