Have you ever wondered how you can use clustering to extract meaningful insight from a time-series single-feature data? In today’s episode, Ehsan speaks about his recent research on actionable feature extraction using clustering techniques. Want to find out more? Listen to discover the methodologies he used for his research and the commensurate results. Visit our website for extended show notes! https://clear.ml/ ClearML is an open-source MLOps solution users love to customize, helping you easil...
Feb 28, 2022•22 min•Transcript available on Metacast Linh Da joins us to explore how image segmentation can be done using k-means clustering. Image segmentation involves dividing an image into a distinct set of segments. One such approach is to do this purely on color, in which case, k-means clustering is a good option. Check out our website for extended show notes and images! Thanks to our Sponsors: Visit Weights and Biases mention Data Skeptic when you request a demo! & Nomad Data In the image below, you can see the k-means clustering segmentati...
Feb 22, 2022•23 min•Transcript available on Metacast In today’s episode, Gregory Glatzer explained his machine learning project that involved the prediction of elephant movement and settlement, in a bid to limit the activities of poachers. He used two machine learning algorithms, DBSCAN and K-Means clustering at different stages of the project. Listen to learn about why these two techniques were useful and what conclusions could be drawn. Click here to see additional show notes on our website! Thanks to our sponsor, Astrato...
Feb 18, 2022•26 min•Transcript available on Metacast Welcome to our new season, Data Skeptic: k-means clustering. Each week will feature an interview or discussion related to this classic algorithm, it's use cases, and analysis. This episode is an overview of the topic presented in several segments.
Feb 14, 2022•24 min•Transcript available on Metacast Frank Bell, Snowflake Data Superhero, and SnowPro, joins us today to talk about his book “Snowflake Essentials: Getting Started with Big Data in the Cloud.” Snowflake Essentials: Getting Started with Big Data in the Cloud by Frank Bell, Raj Chirumamilla, Bhaskar B. Joshi, Bjorn Lindstrom, Ruchi Soni, Sameer Videkar Snowflake Solutions Snoptimizer - Snowflake Cost, Security, and Performance Optimization - Coming Soon! Thanks to our Sponsors: Find Better Data Faster with Nomad Data. Visit nomad-da...
Feb 07, 2022•47 min•Transcript available on Metacast Zack Labe, a Post-Doctoral Researcher at Colorado State University, joins us today to discuss his work “Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles.” Works Mentioned “Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles” by Zachary M. Labe, Elizabeth A. Barnes Sponsored by: Astrato and BBEdit by Bare Bones Software...
Jan 31, 2022•35 min•Transcript available on Metacast Erin Boyle, the Head of Data Science at Myst AI, joins us today to talk about her work with Myst AI, a time series forecasting platform and service with the objective for positively impacting sustainability. https://docs.myst.ai/docs Visit Weights and Biases at wandb.me/dataskeptic Find Better Data Faster with Nomad Data. Visit nomad-data.com...
Jan 24, 2022•43 min•Transcript available on Metacast Sean Law, Principle Data Scientist, R&D at a Fortune 500 Company, comes on to talk about his creation of the STUMPY Python Library. Sponsored by Hello Fresh and mParticle: Go to Hellofresh.com/dataskeptic16 for up to 16 free meals AND 3 free gifts! Visit mparticle.com to learn how teams at Postmates, NBCUniversal, Spotify, and Airbnb use mParticle’s customer data infrastructure to accelerate their customer data strategies....
Jan 17, 2022•39 min•Transcript available on Metacast Data scientists and psychics have at least one major thing in common. Both professions attempt to predict the future. In the case of a data scientist, this is done using algorithms, data, and often comes with some measure of quality such as a confidence interval or estimated accuracy. In contrast, psychics rely on their intuition or an appeal to the supernatural as the source for their predictions. Still, in the interest of empirical evidence, the quality of predictions made by psychics can be p...
Jan 14, 2022•25 min•Transcript available on Metacast Georgia Papacharalampous, Researcher at the National Technical University of Athens, joins us today to talk about her work “Probabilistic water demand forecasting using quantile regression algorithms.” Visit Springboard and use promo code DATASKEPTIC to receive a $750 discount
Jan 10, 2022•26 min•Transcript available on Metacast John Watson, Principal Software Engineer at Splunk, joins us today to talk about Splunk and OpenTelemetry.
Jan 03, 2022•36 min•Transcript available on Metacast Yusan Lin, a Research Scientist at Visa Research, comes on today to talk about her work "Predicting Next-Season Designs on High Fashion Runway."
Dec 27, 2021•35 min•Transcript available on Metacast Time series topics on Data Skeptic predate our current season. This holiday special collects three popular mini-episodes from the archive that discuss time series topics with a few new comments from Kyle.
Dec 25, 2021•37 min•Transcript available on Metacast Dr. Darren Shannon, a Lecturer in Quantitative Finance in the Department of Accounting and Finance, University of Limerick, joins us today to talk about his work "Extending the Heston Model to Forecast Motor Vehicle Collision Rates."
Dec 20, 2021•39 min•Transcript available on Metacast Eric Manibardo, PhD Student at the University of the Basque Country in Spain, comes on today to share his work, "Deep Learning for Road Traffic Forecasting: Does it Make a Difference?"
Dec 13, 2021•32 min•Transcript available on Metacast Daniele Gammelli, PhD Student in Machine Learning at Technical University of Denmark and visiting PhD Student at Stanford University, joins us today to talk about his work "Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management."
Dec 06, 2021•41 min•Transcript available on Metacast Mahdi Abolghasemi, Lecturer at Monash University, joins us today to talk about his work "Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion."
Nov 29, 2021•36 min•Transcript available on Metacast The retail holiday “black Friday” occurs the day after Thanksgiving in the United States. It’s dubbed this because many retail companies spend the first 10 months of the year running at a loss (in the red) before finally earning as much as 80% of their revenue in the last two months of the year. This episode features four interviews with guests bringing unique data-driven perspectives on the topic of analyzing this seeming outlier in a time series dataset.
Nov 26, 2021•45 min•Transcript available on Metacast Alex Terenin, Postdoctoral Research Associate at the University of Cambridge, joins us today to talk about his work "Aligning Time Series on Incomparable Spaces."
Nov 22, 2021•34 min•Transcript available on Metacast 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.
Nov 15, 2021•43 min•Transcript available on Metacast 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."
Nov 08, 2021•31 min•Transcript available on Metacast Bahman Rostami-Tabar, Senior Lecturer in Management Science at Cardiff University, joins us today to talk about his work "Forecasting and its Beneficiaries."
Nov 01, 2021•38 min•Transcript available on Metacast 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."
Oct 25, 2021•38 min•Transcript available on Metacast 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."
Oct 17, 2021•38 min•Transcript available on Metacast 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.”
Oct 11, 2021•41 min•Transcript available on Metacast 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...
Oct 04, 2021•29 min•Transcript available on Metacast 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...
Sep 27, 2021•34 min•Transcript available on Metacast 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.
Sep 20, 2021•24 min•Transcript available on Metacast 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.
Sep 13, 2021•28 min•Transcript available on Metacast 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.
Sep 06, 2021•26 min•Transcript available on Metacast 
