bayesian
modeltime
| bayesian | modeltime | |
|---|---|---|
| 1 | 5 | |
| 42 | 501 | |
| - | 1.0% | |
| 7.7 | 8.4 | |
| 8 days ago | 4 months ago | |
| R | R | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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bayesian
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How do you train a Bayesian model
There's MLMod and Bayesian which are TidyModels wrappers for stan_glm and brms (brms is my go to) but I haven't seen anything that indicates how to tune them.
modeltime
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Cross Validating Time Series Models in R
Check out the ModelTime package: https://business-science.github.io/modeltime/
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Good package or tidy way of sliding time series forecasting windows for backtesting?
I was looking for something similar a bit ago and settled on timetk and modeltime. It's been a while since I worked with these and I never got deep enough in my own project to fully explore them, so unfortunately all I can offer are the links; however this should get you what you're looking for
- Has anyone taken Matt Dancho's courses?
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Time Series in R
It’s actually completely different than what existed. You can see my roadmap here and how much has went into the Modeltime project. https://github.com/business-science/modeltime/issues/5
What are some alternatives?
multilevelmod - Parsnip wrappers for mixed-level and hierarchical models
fable - Tidy time series forecasting
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
Deep_XF - Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
modeltime.gluonts - GluonTS Deep Learning with Modeltime
boostime - The Tidymodels Extension for Time Series Boosting Models
healthyR.ts - A time-series companion package to healthyR
modeltime.resample - Resampling Tools for Time Series Forecasting with Modeltime
Auto_TS - Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.
hal9001 - 🤠 📿 The Highly Adaptive Lasso
modeltime.h2o - Forecasting with H2O AutoML. Use the H2O Automatic Machine Learning algorithm as a backend for Modeltime Time Series Forecasting.
modeltime.ensemble - Time Series Ensemble Forecasting