04-27, 10:15–11:00 (America/Los_Angeles), St. Helens
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).
Conformal prediction is a growing field in machine learning for estimating uncertainty in predictive models. Time series forecasting is a crucial technique in various domains, including finance, energy, and meteorology, but traditional methods often fall short in providing accurate and calibrated estimations of uncertainty. This talk will provide an overview of conformal prediction and its advantages in time series forecasting.
We will cover the use of conformal prediction with different statistical, machine learning, and deep learning models and demonstrate its application in predicting future time series data. The results will be compared to traditional time series forecasting methods and the benefits of using conformal prediction in real-world applications will be discussed.
This talk is designed for data scientists and machine learning practitioners interested in time series who want to learn about the benefits of using conformal prediction for improving uncertainty estimation. The estimated time breakdown for the talk is as follows:
- 5 minutes: Practical and theoretical introduction to conformal prediction
- 25 minutes: Using conformal prediction with statistical and deep learning methods in time series applications
- 5 minutes: Discussion of results and benefits.
No prior knowledge of conformal prediction is required to attend this talk.
No previous knowledge expected
Fede is CTO and co-founder of Nixtla. They has [sic] over five years of experience deploying machine learning models in production, and has worked for large financial institutions in Mexico. An economist and mathematician by training, they passion lies at the intersection of building usable, scalable and open source machine learning products. Speaker at different Pycons.
Max is the CEO and Co-Founder of Nixtla, an open-source time-series research and deployment startup. He is also a seasoned entrepreneur with a proven track record as the founder of multiple technology startups. With a decade of experience in the ML industry, he has extensive expertise in building and leading international data teams. Max has also made notable contributions to the Data Science field through his co-authorship of papers on forecasting algorithms and decision theory. In addition, he is a co-maintainer of several open-source libraries in the Python ecosystem. He has been a speaker at major data conferences in different countries. Max's passion lies at the intersection of business and technology.