tidyr
tidyr replaced separate() with a family that says what it does.
A side-by-side editorial comparison of modeltime and Apache Superset — release velocity, themes, recent moves, and the top alternatives to consider.
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.
Superset's tracked feed is a Helm chart tag stream with no notes attached.
The feed being tracked carries Apache Superset's Helm chart releases rather than Superset itself. Six chart versions landed between mid-July and 10 August, walking 0.21.x up to 0.22.5, and every entry carries only the project's boilerplate one-line description. What actually changed in any given chart bump is not disclosed in the feed.
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.
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.
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.
The feed being tracked carries Apache Superset's Helm chart releases rather than Superset itself. Six chart versions landed between mid-July and 10 August, walking 0.21.x up to 0.22.5, and every entry carries only the project's boilerplate one-line description. What actually changed in any given chart bump is not disclosed in the feed.
Chart releases are arriving roughly weekly, which points to steady packaging maintenance underneath — image bumps, template corrections, values-file changes — rather than a visible product push. Because the feed publishes no notes, the deployment layer is the only Superset surface observable here. Anyone tracking application-level work needs the core repository, not this stream.
The cadence supports exactly one confident call: more chart point releases within weeks. The entries carry no content indicating what those bumps will contain.
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 Apache Superset.
tidyr replaced separate() with a family that says what it does.
performance keeps adding ways to check a model you have already fitted.
CmdStanPy is clearing deprecations ahead of a 2.0 it keeps announcing.
DoWhy adds one estimation method a year and keeps its identification edge.
OpenHouse is hardening the seams where table policies and jobs quietly fail.
silx 3.0 moved its default Qt binding to PySide6 — a migration for everyone embedding it.
See all modeltime alternatives → · See all Apache Superset alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Apache Superset is currently shipping more aggressively (velocity 5.0 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.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Apache Superset is currently shipping more aggressively (velocity 5.0 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.
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.
Top Apache Superset alternatives in Analytics are ranked by recent ship velocity. Browse the "Apache Superset alternatives" section above for the current picks, or visit /alternatives/superset for the full list with editorial commentary on each.