When data is collected sequentially or a measurement is taken periodically in time, the result is time series data. Time series is typically separated into the topics of analysis and forecasting. This season of Data Skeptic explores the tools, methodologies, and use cases for time series problems.
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.
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.
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.
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.
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?”
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.
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.”
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.
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.
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.
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.
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.
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.
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.”
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.
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.
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.
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.
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.
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".
Sankeerth Rao Karingula, ML Researcher at Palo Alto Networks, joins us today to talk about his work “Boosted Embeddings for 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.”
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."
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."
Bahman Rostami-Tabar, Senior Lecturer in Management Science at Cardiff University, joins us today to talk about his work "Forecasting and its Beneficiaries."
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."
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.
Alex Terenin, Postdoctoral Research Associate at the University of Cambridge, joins us today to talk about his work "Aligning Time Series on Incomparable Spaces."
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.
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."
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."
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?"
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."
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.
Yusan Lin, a Research Scientist at Visa Research, comes on today to talk about her work "Predicting Next-Season Designs on High Fashion Runway."
John Watson, Principal Software Engineer at Splunk, joins us today to talk about Splunk and OpenTelemetry.
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.”
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 put to the test.
Sean Law, Principle Data Scientist, R&D at a Fortune 500 Company, comes on to talk about his creation of the STUMPY Python Library.
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
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.”
“Detecting Climate Signals using Explainable AI with Single Forcing Large Ensembles”
by Zachary M. Labe, Elizabeth A. Barnes
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