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[Online] Time Series Analysis with Bayesian State Space Models in PyMC
2024-08-26 · 16:00
🎙️ Speaker: Jesse Grabowski \| ⏰ Time: 16:00 UTC / 9:00 am PT / 12:00 pm ET / 6:00 pm Berlin Time series are everywhere, and building time into our models can bring them to the next level. Modeling time series, however, can be a minefield. They can be especially hard in PyMC, where one needs to have deep knowledge of both pytensor and PyMC internals to set up recursive models, handle missing data, make out of sample forecasts, or generate IRFs. This talk aims to introduce PyMC users to the pymc-statespace module, a collection of tools designed to help users past these hurdles and into time series modeling. The talk will introduce the linear gaussian state space framework, and give end-to-end bayesian time series workflow examples. This will include pre-defined models like SARIMAX, modular structural time series models built from latent components, and an example of implementing a complex custom model, using a Dynamic Stochastic General Equilibrium (DSGE) as a motivating example. ResourcesExample notebooks in pymc-experimental: 📜 Outline of Talk / Agenda:
💼 About the speaker:
🔗 Connect with Jesse: 👉 Linkedin: https://www.linkedin.com/in/jessegrabowski/ 👉 Github: https://github.com/jessegrabowski 👉 Youtube: https://www.youtube.com/@Thayme 👉 Website: http://jbgrabowski.com/ 💼 About the Host:
🔗 Connect with Thomas: 👉 Linkedin: https://www.linkedin.com/in/twiecki/ 👉 GitHub: https://github.com/twiecki 👉 Twitter: https://twitter.com/twiecki 👉 Website: https://www.pymc-labs.com/ https://twiecki.io/ 📖 Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct. 🔗 Connecting with PyMC Labs: 🌐 Website: https://www.pymc-labs.com/ 👥 LinkedIn: https://www.linkedin.com/company/pymc-labs/ 🐦 Twitter: https://twitter.com/pymc_labs 🎥 YouTube: https://www.youtube.com/c/PyMCLabs 🤝 Meetup: https://www.meetup.com/pymc-labs-online-meetup/ |
[Online] Time Series Analysis with Bayesian State Space Models in PyMC
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