modeltime
hal9001
| modeltime | hal9001 | |
|---|---|---|
| 5 | 1 | |
| 499 | 48 | |
| 0.6% | - | |
| 8.4 | 5.2 | |
| 4 months ago | 13 days ago | |
| R | R | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
modeltime
-
Cross Validating Time Series Models in R
Check out the ModelTime package: https://business-science.github.io/modeltime/
-
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?
-
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
hal9001
-
[Q] Should G-methods, IPTW always be used over traditional regression?
Another approach is to make your own SL learner. It turns out to be not as difficult as it may seem to do this. You still pass in the same character string to the SuperLearner functions (e.g. "SL.customlearner") and it will extract the function "SL.customlearner" from your R environment. Here is one example: https://github.com/tlverse/hal9001/blob/devel/R/sl_hal9001.R
What are some alternatives?
fable - Tidy time series forecasting
causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
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.
yaglm - A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
modeltime.gluonts - GluonTS Deep Learning with Modeltime
tmlenet - Targeted Maximum Likelihood Estimation for Network Data
boostime - The Tidymodels Extension for Time Series Boosting Models
lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:
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.