Matt Dancho

**Rank Deficient Matrix** - This comes from the linear regression because the number of post-processed features is large causing the number of columns to exceed the number of rows in...

I've been thinking about this one too. Calibration and forecasting can be expensive too and should be able to be parallelized using the same patterns we have implemented in the...

I've reopened the issue to keep it for tracking. The main actions are: - [ ] Parallel Calibration: `modeltime_calibrate()` - [ ] Parallel Forecasting: `modeltime_forecast()`

GPU support is dependent on the algorithm and your machine. - XGBoost can be run on GPU if properly set up. https://github.com/dmlc/xgboost/blob/master/R-package/demo/gpu_accelerated.R AND something to consider is how GPU vs...

Thanks for this @JustinKurland. I am working on a side-project now that I believe will help users implement production model selection with `yardstick` accuracy metrics much easier from `modeltime`. However,...

This sounds like a great improvement idea. I'd need to find out more but I like the idea.

Yes, I agree there are parallels. I'm not sure what specifically to look at compared to `fable`. I do plan to incorporate most of the `forecast` package functionality into `modeltime`....

I’m a big fan of `fable`, I just couldn’t get it to work for anything using machine learning and deep learning. The `tidymodels` system is an amazing evolution of caret...

**TBATS Model** TBATS is added to the `seasonal_reg()`, seasonal time series regression model specification. ``` seasonal_reg( seasonal_period_1 = 7, seasonal_period_2 = 30, seasonal_period_3 = 365 ) %>% set_engine("tbats") ```

## Deep Learning (RNN / LSTM) with Modeltime GluonTS The `modeltime.gluonts` library has been created to handle Deep Learning by integrating the Time Series Deep Learning Library, GluonTS. Resources: -...