Today we are joined again by Ben Fulcher, leader of the Dynamics and Neural Systems Group at the University of Sydney in Australia, to talk about hctsa, a software package for running highly comparative time-series analysis.
Gerrit van den Burg, Postdoctoral Researcher at The Alan Turing Institute, joins us today to discuss his work "An Evaluation of Change Point Detection Algorithms."
Bahman Rostami-Tabar, Senior Lecturer in Management Science at Cardiff University, joins us today to talk about his work "Forecasting and its Beneficiaries."
Alex Mallen, Computer Science student at the University of Washington, and Henning Lange, a Postdoctoral Scholar in Applied Math at the University of Washington, join us today to share their work "Deep Probabilistic Koopman: Long-term Time-Series Forecasting Under Periodic Uncertainties."
Fotios Petropoulos, Professor of Management Science at the University of Bath in The U.K., joins us today to talk about his work "Fast and Frugal Time Series Forecasting."
Manie Tadayon, a PhD graduate from the ECE department at University of California, Los Angeles, joins us today to talk about his work "Comparative Analysis of the Hidden Markov Model and LSTM: A Simulative Approach."
Sankeerth Rao Karingula, ML Researcher at Palo Alto Networks, joins us today to talk about his work "Boosted Embeddings for Time Series Forecasting." Works Mentioned Boosted Embeddings for Time Series Forecasting by Sankeerth Rao Karingula, Nandini Ramanan, Rasool Tahmasbi, Mehrnaz Amjadi, Deokwoo Jung, Ricky Si, Charanraj Thimmisetty, Luisa Polania Cabrera, Marjorie Sayer, Claudionor Nunes Coelho Jr https://www.linkedin.com/in/sankeerthrao/ https://twitter.com/sankeerthrao3 https://lod2021.icas...
David Daly, Performance Engineer at MongoDB, joins us today to discuss "The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System". Works Mentioned The Use of Change Point Detection to Identify Software Performance Regressions in a Continuous Integration System by David Daly, William Brown, Henrik Ingo, Jim O'Leary, David BradfordSocial Media David's Website David's Twitter Mongodb...
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.
Dr. Feng Li, (@f3ngli) is an Associate Professor of Statistics in the School of Statistics and Mathematics at Central University of Finance and Economics in Beijing, China. He joins us today to discuss his work Distributed ARIMA Models for Ultra-long Time Series.
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, gaining a 75x improvement on larger datasets with essentially the same performance. In this episode, we talk about the insights that realized this speedup as well as use cases.
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.
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
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/
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.
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.
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 especia...
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.
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...
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 effectiveness. Carl Stimson, a Freelance Japanese to English translator, comes on the show to talk about his work in translation and his perspective about how AI will change translation in the future.
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."
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
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?"
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/haifa/dept/vst/ML-QA.shtml
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.
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.
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.
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 true statistic hidden in a list of statistics which are off by at least an order of magnitude. Claudia and Vanessa join as our first contestants. Below are the sources of our questions. Heights https://en.wikipedia.org/wi...
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 effectiveness. Unless progress in AI inexplicably halts, the tasks done by humans vs. machines will continue to evolve. Today's episode is a speculative conversation about what the future may hold. Co-Host of Squaring the Strange Podcast, Caricature Artist, and an Academic Editor, Celestia Ward joins us...
Today on the show Derek Driggs, a PhD Student at the University of Cambridge. He comes on to discuss the work Common Pitfalls and Recommendations for Using Machine Learning to Detect and Prognosticate for COVID-19 Using Chest Radiographs and CT Scans. Help us vote for the next theme of Data Skeptic! Vote here : https://dataskeptic.com/vote
