Ultra Long Time Series

Ultra Long Time Series

Applying k-Nearest Neighbors to Time Series

Samya Tajmouati, a PhD student in Data Science at the University of Science of Kenitra, Morocco, joins us today to discuss her work Applying K-Nearest Neighbors to Time Series Forecasting: Two New Approaches. 

MiniRocket

Angus Dempster, PhD Student at Monash University in Australia, comes on today to talk about MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification, a fast deterministic transform for time series classification. MINIROCKET reformulates ROCKET, gain ...  Show more

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. 

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 

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/ ...  Show more

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. 

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. 

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 ...  Show more

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. Predic ...  Show more

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 A ...  Show more

Translation Automation

Today we are back with another episode discussing AI in the work field. AI has, is, and will continue to facilitate the automation of work done by humans. Sometimes this may be an entire role. Other times it may automate a particular part of their role, scaling their effectivenes ...  Show more

Time Series at the Beach

Shane Ross, Professor of Aerospace and Ocean Engineering at Virginia Tech University, comes on today to talk about his work “Beach-level 24-hour forecasts of Florida red tide-induced respiratory irritation.” 

Automatic Identification of Outlier Galaxy Images

Lior Shamir, Associate Professor of Computer Science at Kansas University, joins us today to talk about the recent paper Automatic Identification of Outliers in Hubble Space Telescope Galaxy Images. Follow Lio on Twitter @shamir_lior 

Do We Need Deep Learning in Time Series

Shereen Elsayed and Daniela Thyssens, both are PhD Student at Hildesheim University in Germany, come on today to talk about the work “Do We Really Need Deep Learning Models for Time Series Forecasting?” 

Detecting Drift

Sam Ackerman, Research Data Scientist at IBM Research Labs in Haifa, Israel, joins us today to talk about his work Detection of Data Drift and Outliers Affecting Machine Learning Model Performance Over Time. Check out Sam's IBM statistics/ML blog at: http://www.research.ibm.com/h ...  Show more

Darts Library for Time Series

Julien Herzen, PhD graduate from EPFL in Switzerland, comes on today to talk about his work with Unit 8 and the development of the Python Library: Darts.  

Forecasting Principles and Practice

Welcome to Timeseries! Today’s episode is an interview with Rob Hyndman, Professor of Statistics at Monash University in Australia, and author of Forecasting: Principles and Practices. 

Prequisites for Time Series

Today's experimental episode uses sound to describe some basic ideas from time series. This episode includes lag, seasonality, trend, noise, heteroskedasticity, decomposition, smoothing, feature engineering, and deep learning.   

Orders of Magnitude

Today’s show in two parts. First, Linhda joins us to review the episodes from Data Skeptic: Pilot Season and give her feedback on each of the topics. Second, we introduce our new segment “Orders of Magnitude”. It’s a statistical game show in which participants must identify the t ...  Show more