← Back to home
Comparison · Analytics

modeltime vs iris

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

modeltime vs iris: at a glance

Featuremodeltimeiris
SectorAnalyticsAnalytics
Velocity score0.02.5
Sparks · 30d00
Top themesforecasting, conformal-prediction, tidymodels, parallelismearth science, release cadence, python, release candidates
Last editorial update54m ago2h ago
WebsiteVisit →Visit →

What is modeltime?

modeltime built conformal intervals in, then went quiet on features.

modeltime is at 1.3.3, a single change making the package robust to xgboost version shifts. The feature weight sits in 1.3.2, which added a future-based parallel backend, the maape() accuracy metric and dials helpers for ADAM engine tuning, and further back in the 1.2.8 and 1.3.0 pair that introduced conformal prediction intervals and then carried them through the nested forecasting workflow.

Read the full modeltime trajectory →

What is iris?

Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.

Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.

Read the full iris trajectory →

modeltime vs iris: editorial side-by-side

M
modeltime
ANALYTICS
0.0

modeltime built conformal intervals in, then went quiet on features.

◆ Current state

modeltime is at 1.3.3, a single change making the package robust to xgboost version shifts. The feature weight sits in 1.3.2, which added a future-based parallel backend, the maape() accuracy metric and dials helpers for ADAM engine tuning, and further back in the 1.2.8 and 1.3.0 pair that introduced conformal prediction intervals and then carried them through the nested forecasting workflow.

◆ Where it's heading

The arc runs from uncertainty quantification to execution. Conformal intervals arrived first and were then threaded through nested fitting, refitting and the printed forecast tables so users can see which confidence method produced an interval. The later work moves down a layer to how forecasts are computed — a portable future backend replacing foreach tuning — rather than what they express.

◆ Prediction

With only an xgboost compatibility fix since the 1.3.2 feature release, the entries do not support a confident prediction about what comes next beyond continued dependency maintenance.

I
iris
ANALYTICS
2.5

Iris ships steadily on a two-a-year cadence, but its feed publishes only pointers.

◆ Current state

Iris tags a release candidate roughly every four to five months — 3.13 through 3.16 over the past year — and the cadence is the only thing the feed actually reports. Every entry is the same seven-line template: a line saying this is a release candidate, conda-forge and PyPI install commands, and a link to a 'What's New' page held elsewhere. No release notes reach the feed at all.

◆ Where it's heading

The version numbers say a mature Met Office library is being maintained on a predictable schedule; nothing in the published entries says what is being maintained. Until the project puts release content in the tag body, its public trail will read as cadence without substance, and readers have to leave the feed to learn anything. The pattern has been identical across four consecutive releases, so it is a deliberate publishing choice rather than an oversight.

◆ Prediction

Expect v3.17.0rc0 around late 2026 on the same schedule, carrying the same boilerplate — the notes will again live on the documentation site rather than in the release entry.

Alternatives to modeltime and iris

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 modeltime or iris.

See all modeltime alternatives → · See all iris alternatives →

Recent activity from modeltime and iris

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

  1. 13d agoirisv3.16.0rc0
  2. 4mo agoirisv3.15.0rc0
  3. 7mo agomodeltimeRobustness to xgboost version changes
  4. 9mo agoirisv3.14.0rc0
  5. 11mo agomodeltimefuture parallel backend, maape() metric and ADAM tuning helpers
  6. 1y agoirisv3.13.0rc0
  7. 2y agomodeltimeConformal intervals reach the nested forecasting workflow
  8. 2y agomodeltimeConformal prediction intervals introduced
  9. 3y agomodeltimeFixes the Smooth es() model
  10. 3y agomodeltimeFixes failing developer-tools tests

Frequently asked questions

What is the difference between modeltime and iris?

They serve adjacent needs but don't currently overlap on shipped themes. iris is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is modeltime better than iris?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. iris is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to modeltime?

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

What are the best alternatives to iris?

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