Just 3 days ago, I had the pleasure of watching the #rstudioconf2022 kick off.
I've been attending since 2018 and watching even longer than that.
And, I was just a normal spectator in the audience until this happened.
@topepos and @juliasilge's keynote showed all of the open source work their team has been working on to build the best machine learning ecosystem in R called #tidymodels.
And then they brought this slide up.
Max and Julia then proceeded to talk about how the community members have been working on expanding the ecosystem.
- Text Recipes for Text
- Censored for Survival Modeling
- Stacks for Ensembles
And then they announced me and my work on Modeltime for Time Series!!!
I had no clue this was going to happen.
Just a spectator in the back.
My friends to both sides went nuts. Hugs, high-fives, and all.
My students in my slack channel went even more nuts.
Throughout the rest of the week, I was on cloud-9.
My students that were at the conf introduced themselves.
Much of our discussions centered around Max & Julia's keynote and the exposure that modeltime got.
And all of this wouldn't be possible without the support of this company. Rstudio / posit.
So, I'm honored to be part of something bigger than just a programming language.
And if you'd like to learn more about what I do, I'll share a few links.
The first is my modeltime package for #timeseries.
This has been a 2-year+ passion project for building the premier time series forecasting system.
It now has multiple extensions including ensembles, resampling, deep learning, and more.
Skforecast is a Python library for time series forecasting using machine learning models.
Skforecast works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.
skforecast shines with probabilistic forecasting.
When trying to anticipate future values, most forecasting models try to predict what will be the most likely value.
BEAST: A Bayesian Ensemble Algorithm for Change-Point Detection and Time Series Decomposition in R
Let's explore:
1. What is BEAST?
BEAST stands for Bayesian Estimator of Abrupt change, Seasonality, and Trend.
But what does that mean?
According to the authors, BEAST is a fast, generic Bayesian model averaging algorithm to decompose time series or 1D sequential data into individual components, such as abrupt changes, trends, and periodic/seasonal variations