---
title: "Boris Guarisma (@bguarisma) | Hashnode"
description: "Data Science, AI & Quantum Computing. Read the latest articles by Boris Guarisma on Hashnode."
url: "https://hashnode.com/@bguarisma"
---

@bguarisma

# Boris Guarisma

@bguarisma · Paris, France · Joined December 2021

Data Science, AI & Quantum Computing

[Website](https://qognito.io/)

## Comments

[John Rambo Thanks ! FYI the article has been updated, mostly at the end (final forecast) where I've skipped an important part.](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea/comment/61ec80fea12088017fbea8b3#reply-61f10e37e247b2151e620b6c)

Reply · Article · Jan 26, 2022 · [Time Series Forecasting Lab (Part 6) - Stacked Ensembles](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea)

[Simona Jokubauskaite yes, my bad, I've skipped an important part, the article has been updated. It was my last article ... I was getting tired. Thanks !](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea/comment/61f005a887fd2728e7cd34d6#reply-61f10d1f87fd2728e7cd51ef)

Reply · Article · Jan 26, 2022 · [Time Series Forecasting Lab (Part 6) - Stacked Ensembles](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea)

[It's there :-)forecast\_stacking\_tbl <- bind\_rows(lforecasts) Thanks anyway !](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea/comment/61f005a887fd2728e7cd34d6#reply-61f0099387fd2728e7cd3540)

Reply · Article · Jan 25, 2022 · [Time Series Forecasting Lab (Part 6) - Stacked Ensembles](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea)

[Thank you Simona once again. The article has been corrected, "phtxb" should not be used and "model\_spec\_rf\_tune" was for random forest, here we work with prophet boost.](https://hashnode.com/posts/time-series-forecasting-lab-part-4-hyperparameter-tuning/61e2ebb11f35f37864469c60/comment/61efd55a87fd2728e7cd2e3b#reply-61efd8c487fd2728e7cd2ea1)

Reply · Article · Jan 25, 2022 · [Time Series Forecasting Lab (Part 4) - Hyperparameter Tuning](https://hashnode.com/posts/time-series-forecasting-lab-part-4-hyperparameter-tuning/61e2ebb11f35f37864469c60)

[Thank you Simona Jokubauskaite ! I will update by referring the reader to your comment](https://hashnode.com/posts/time-series-forecasting-lab-part-2-feature-engineering-with-recipes/61d5c777e2f29f0805e13d7a/comment/61eec6d9c99aed184e7275c7#reply-61eedeb7c99aed184e727948)

Reply · Article · Jan 24, 2022 · [Time Series Forecasting Lab (Part 2) - Feature Engineering with Recipes](https://hashnode.com/posts/time-series-forecasting-lab-part-2-feature-engineering-with-recipes/61d5c777e2f29f0805e13d7a)

[Hi John, thank you for your message. Indeed, I forgot to explain how the resampled predictions are obtained, I have inserted a new code snippet above, see "Generate predictons for meta-learners". Use function modeltime\_fit\_resamples() which uses the k-fold resamples from vfold\_cv().](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea/comment/61ec80fea12088017fbea8b3#reply-61ed1daba12088017fbeb6ee)

Reply · Article · Jan 23, 2022 · [Time Series Forecasting Lab (Part 6) - Stacked Ensembles](https://hashnode.com/posts/time-series-forecasting-lab-part-6-stacked-ensembles/61e5ecea00c806252387c7ea)