The Right (big data) Tool for the Job with Jay Shankar
Data Skeptic7 Juli 2014

The Right (big data) Tool for the Job with Jay Shankar

In this week's episode, we discuss applied solutions to big data problem with big data engineer Jay Shankar. The episode explores approaches and design philosophy to solving real world big data business problems, and the exploration of the wide array of tools available.

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ARiMA is not Sufficient

ARiMA is not Sufficient

Chongshou Li, Associate Professor at Southwest Jiaotong University in China, joins us today to talk about his work Why are the ARIMA and SARIMA not Sufficient.

30 Aug 202122min

Comp Engine

Comp Engine

Ben Fulcher, Senior Lecturer at the School of Physics at the University of Sydney in Australia, comes on today to talk about his project Comp Engine. Follow Ben on Twitter: @bendfulcher For posts about time series analysis : @comptimeseries comp-engine.org

23 Aug 202136min

Detecting Ransomware

Detecting Ransomware

Nitin Pundir, PhD candidate at University Florida and works at the Florida Institute for Cybersecurity Research, comes on today to talk about his work "RanStop: A Hardware-assisted Runtime Crypto-Ransomware Detection Technique." FICS Research Lab - https://fics.institute.ufl.edu/ LinkedIn - https://www.linkedin.com/in/nitin-pundir470/

16 Aug 202131min

GANs in Finance

GANs in Finance

Florian Eckerli, a recent graduate of Zurich University of Applied Sciences, comes on the show today to discuss his work Generative Adversarial Networks in Finance: An Overview.

9 Aug 202123min

Predicting Urban Land Use

Predicting Urban Land Use

Today on the show we have Daniel Omeiza, a doctoral student in the computer science department of the University of Oxford, who joins us to talk about his work Efficient Machine Learning for Large-Scale Urban Land-Use Forecasting in Sub-Saharan Africa.

2 Aug 202127min

Opportunities for Skillful Weather Prediction

Opportunities for Skillful Weather Prediction

Today on the show we have Elizabeth Barnes, Associate Professor in the department of Atmospheric Science at Colorado State University, who joins us to talk about her work Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. Find more from the Barnes Research Group on their site. Weather is notoriously difficult to predict. Complex systems are demanding of computational power. Further, the chaotic nature of, well, nature, makes accurate forecasting especially difficult the longer into the future one wants to look. Yet all is not lost! In this interview, we explore the use of machine learning to help identify certain conditions under which the weather system has entered an unusually predictable position in it's normally chaotic state space.

26 Juli 202134min

Predicting Stock Prices

Predicting Stock Prices

Today on the show we have Andrea Fronzetti Colladon (@iandreafc), currently working at the University of Perugia and inventor of the Semantic Brand Score, joins us to talk about his work studying human communication and social interaction. We discuss the paper Look inside. Predicting Stock Prices by Analyzing an Enterprise Intranet Social Network and Using Word Co-Occurrence Networks.

19 Juli 202134min

N-Beats

N-Beats

Today on the show we have Boris Oreshkin @boreshkin, a Senior Research Scientist at Unity Technologies, who joins us today to talk about his work N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. Works Mentioned: N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting By Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados, Yoshua Bengio https://arxiv.org/abs/1905.10437 Social Media Linkedin Twitter

12 Juli 202134min

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