Max Mergenthaler and Fede Garza - Quantifying Uncertainty in Time Series Forecasting
www.pydata.org This talk will examine the use of conformal prediction in the context of time series analysis. The presentation will highlight the benefits of using conformal prediction to estimate uncertainty and demonstrate its application using open source python libraries for statistical, machine learning, and deep learning models (https://github.com/Nixtla). PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...
Eng & Kwon - Scaling data workloads using the best of both worlds: pandas and Spark
Juan Orduz - Time Series forecasting with NumPyro | PyData Amsterdam 2024
A Tutorial on Conformal Prediction
Preserving Approval Evidence | Trustify Legal Workflow
Kishan Manani - Feature Engineering for Time Series Forecasting | PyData London 2022
Nixtla: tools for timeseries
TimeGPT, Nixtla & Forecasting with Max Mergenthaler #53
Cordier & Lacombe - Boosting AI Reliability: Uncertainty Quantification with MAPIE
Hierarchical Forecasting in Python | Nixtla
Kishan Manani- Backtesting and error metrics for modern time series forecasting | PyData London 2024
Jonathan Bechtel - Forecasting With Classical and Machine Learning Methods | PyData NYC 2023
Beyond Toy Datasets: Timeseries Forecasting for Real Business Problems - Robert Haase
Emmanuel Candès - A Taste of Conformal Prediction
Uncertainty Quantification (1): Enter Conformal Predictors
Nixtla: Deep Learning for Time Series Forecasting
Hierarchical Time Series With Prophet and PyMC (Matthijs Brouns)
Challenges in Time Series Forecasting
Adaptive Conformal Predictions for Time Series | ISDFS
Professor Marcos Lopez de Prado on Machine Learning in Asset Management - Kapitaleierdagen 2024
