Automatically Find Patterns & Anomalies from Time Series or Sequential Data - Sean Law
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- čas přidán 2. 07. 2019
- In this talk, you’ll learn of a brand new and scalable approach to explore time series or sequential data. If anybody has ever asked you to analyze time series data and to look for new insights then this is definitely the open source tool that you’ll want to add to your arsenal.
- Věda a technologie
we need MOAR talks like this! amazing!
Stamped with your presentation skill. All clear and lucid and 100 slides. 🎉❤🎉
I've never thought about "no free lunch" the way he explained it, that there's always a price to pay in the form of a tradeoff, when you gain performance here you're likely loosing something else there.
I always looked at it from the perspective that you can't just blindly apply a model on a dataset and expect the model to do all the work for you without any more input from you.
Very clear explanation, I'm not a native english speaker but I understood everything clearly.
This is the best and easy way to find an anomalies but it's effective thanks Sean law introducing open source library stumpy
nice explanation and great work, may be this is what me and my team were looking for thanks man!
Thank you for explaining it well. I was searching for something with respect to biological data and may be I can use it. Great talk..
Have you had success?
Great Video and better than great even explained! Thanks
I like how the word MOAR is the most ENLARGED TEXT lollll
Great Presentation... RIP TD Ameritrade
at 15:50 the matrix profile looks like it is not plotted as is - based on the second subplot, the Y axis is between 0.5 and 2.5 whereas the matrix profile has values ranging from 1.4 to 14.1- what kind of processing has been done on the matrix profile before plotting it?
16:22 .. I thought that guy was asleep lol
Sounds cool
Great presentation; is it possible to implement it in sequence log data clustering?
Hello, we would advise reaching out to the speaker, Sean Law, directly.
Can this be done using SPSS?
robust approach simplified.