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 (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 TH-cam videos to help with discoverability? Find out more here: github.com/numfocus/TH-camVi...

[@duynguyen4154]

Really interesting library. I have questions which maybe someone can answer here: 1) Can we use our custom model like neural network or LSTM? and 2) How this library assure the time series is exchangeability for the conformal prediction?

[@mapi55555]

Is there a video for anomaly detection using Nixtla?

[@paxcema]

Awesome talk!

[@laavispamaya]

Great talk, tons of thanks

[@AnaSofiaVazquezG]

Nixtla

[@dennis_doom]

How can we provide confidence intervals for classic statistical models that are not stochastic?