[ds-undergrads] [Fwd: [UCR_DataScience] Data Science talk by Prof. Eamonn Keogh, Friday September 30th, 12-1pm, MRB Seminar Room]

[tsotras at cs.ucr.edu]

Dear DS students,
welcome back and I hope you have a good quarter start!

Below is the info about the Data Science seminar next Friday, Sept 30th,
12-1pm at the MRB seminar room (pizza and refreshments are provided). The
talk is by one of the DS faculty, Prof. Eamonn Keogh.
If you plan to attend please register at the link below.

best,
V. Tsotras



---------------------------- Original Message ----------------------------
Subject: [UCR_DataScience] Data Science talk by Prof. Eamonn Keogh, Friday
September 30th, 12-1pm, MRB Seminar Room
From:    tsotras at cs.ucr.edu
Date:    Fri, September 23, 2022 4:39 pm
To:      datascience at lists.ucr.edu
--------------------------------------------------------------------------

The Data Science seminars for Fall 2022 start next Friday, September 30th,
2022.
The seminar takes place 12:00-1:00pm at the MRB Seminar Room (1st floor).

**** Pizza and refreshments will be provided ****

To keep track of the number of attendees, please *register* at:
https://www.eventbrite.com/e/data-science-talk-tickets-425985081847

The talk will be given by Eamonn Keogh, Distinguished Professor,
Department of Computer Science and Engineering, UCR

Title:
Why 95% of papers on Time Series Anomaly Detection are Wrong (with more
general lessons for Researchers).

Abstract:

Time Series Anomaly Detection (TSAD) is the task of monitoring a time
series, say an ECG, or the pressure in an industrial boiler, while
attempting to recognize when there has been an anomalous event. The
anomalies could be the beginning of heart attack, or a leak in the boiler
that will cause the industrial product to spoil. TSAD is a commercially
important problem, by most estimates worth billions of dollars each year.
In the last few years there has been an explosion of interest in TSAD,
with dozens of papers appearing in the top conferences and journals each
year.

In this talk I will make a surprising claim. At least 95% of the papers on
TSAD are deeply flawed, and are at best unreliable. These flaws come from
two major sources, using unsuitable metrics of success, and testing on
flawed benchmark datasets. I will demonstrate my claims with forcefully,
simple, visual examples. I will then go on to suggest some simple fixes
for these issues. I will conclude by briefly touching on two interesting
meta-questions. How did the research community not spot these issues
before? And, what does it say that when these issues are pointed out, most
of the community offers no counterarguments, but just ignores the problem
(the head-in-the-sand response).

Joint work with Renjie Wu.


------------------------------------
Sponsored by the UCR Data Science Center, the purpose of the Data Science
talks is to foster collaborations between "core" Data Science faculty
(from CSE/ECE/Stat Departments) and faculty/visitors from other sciences
that face Data Science problems in their research. These informal
gatherings are open to interested faculty and graduate students. Each
meeting will start with a talk describing research problems and then a
discussion will follow for questions, open problems, ideas for possible
collaborations etc.

A full list of previous seminars appears at:
http://datascience.ucr.edu/seminars

Please forward this email to other colleagues or graduate students in your
lab that may be interested.

Moreover, if you are interested in giving a Data Science related talk,
please contact me.

Sincerely,
Vassilis Tsotras
Professor, CSE Department
Director, Data Science Major







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