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filtro vs modeltime.ensemble

A side-by-side editorial comparison of filtro and modeltime.ensemble — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:tidymodelsr package

filtro vs modeltime.ensemble: at a glance

Featurefiltromodeltime.ensemble
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesfeature selection, tidymodels, s7, filter methodstime series forecasting, ensembles, tidymodels, compatibility maintenance
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is filtro?

filtro moves to S7 and multiplies its feature-scoring methods in a single release.

filtro supplies feature-selection filter scores for the tidymodels stack. Version 0.2.0 adds five scoring methods — correlation, random forest importance, information gain, ROC AUC and cross tabulation — and moves the package from S3 to S7. It also gains a ranking layer: show_best_score_* and rank_best_score_* helpers for a single score, plus desirability-function helpers for optimising across several scores at once.

Read the full filtro trajectory →

What is modeltime.ensemble?

modeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.

modeltime.ensemble builds average, weighted and stacked ensembles over modeltime forecast models. After a four-year gap it shipped twice in a fortnight during August and September 2025, both releases devoted to tracking breaking changes in tidymodels' tune package — new resampling column conventions, key uniqueness across resamples, recipe preparation. The tidyverse dependency was dropped in the same pass.

Read the full modeltime.ensemble trajectory →

filtro vs modeltime.ensemble: editorial side-by-side

F
filtro
ANALYTICS
0.0

filtro moves to S7 and multiplies its feature-scoring methods in a single release.

◆ Current state

filtro supplies feature-selection filter scores for the tidymodels stack. Version 0.2.0 adds five scoring methods — correlation, random forest importance, information gain, ROC AUC and cross tabulation — and moves the package from S3 to S7. It also gains a ranking layer: show_best_score_* and rank_best_score_* helpers for a single score, plus desirability-function helpers for optimising across several scores at once.

◆ Where it's heading

The package is being built out on two axes at once — the catalogue of scores, and the machinery for choosing between them. The desirability functions are the more telling half, since they assume users will filter on several criteria rather than one. Adopting S7 while still pre-1.0 suggests the object model is being settled before the API is frozen.

◆ Prediction

Expect more scoring methods on the same S7 interface and a 1.0 release once the score and ranking APIs stop moving; the ranking helpers' naming is the most likely thing to change first.

M0.0

modeltime.ensemble wakes after four years, and the work is all tune 2.0 compatibility.

◆ Current state

modeltime.ensemble builds average, weighted and stacked ensembles over modeltime forecast models. After a four-year gap it shipped twice in a fortnight during August and September 2025, both releases devoted to tracking breaking changes in tidymodels' tune package — new resampling column conventions, key uniqueness across resamples, recipe preparation. The tidyverse dependency was dropped in the same pass.

◆ Where it's heading

This is a package whose forecasting capability was settled by 2021 — recursive ensembles, per-series calibration — and whose recent life is dictated entirely by upstream tidymodels churn. New contributors did that compatibility work, including one from the tidymodels side. It now requires tune 2.0.0 and modeltime.resample 0.3.0, pinning it to the current tidymodels generation rather than straddling versions.

◆ Prediction

Expect the next release to follow the next tune or modeltime.resample breaking change rather than to introduce new ensembling methods.

Alternatives to filtro and modeltime.ensemble

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either filtro or modeltime.ensemble.

See all filtro alternatives → · See all modeltime.ensemble alternatives →

Recent activity from filtro and modeltime.ensemble

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 11mo agomodeltime.ensembleRealigned for tune 2.0.0 resampling changes
  2. 11mo agomodeltime.ensembleDrops the tidyverse dependency ahead of tune 2.0
  3. 11mo agofiltroFive new filter scores and the move to S7
  4. 11mo agofiltroDevelopment build: score transformation handling
  5. 5y agomodeltime.ensemblePer-series calibration IDs and parallel refitting
  6. 5y agomodeltime.ensembleRecursive ensembles for single and panel series

Frequently asked questions

What is the difference between filtro and modeltime.ensemble?

Both compete on the same themes — tidymodels, r package — within Analytics. filtro and modeltime.ensemble are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is filtro better than modeltime.ensemble?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. filtro and modeltime.ensemble are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to filtro?

Top filtro alternatives in Analytics are ranked by recent ship velocity. Browse the "filtro alternatives" section above for the current picks, or visit /alternatives/filtro-r for the full list with editorial commentary on each.

What are the best alternatives to modeltime.ensemble?

Top modeltime.ensemble alternatives in Analytics are ranked by recent ship velocity. Browse the "modeltime.ensemble alternatives" section above for the current picks, or visit /alternatives/modeltime-ensemble for the full list with editorial commentary on each.