Changelog

v0.3 (2026-07-04)

TimeSeriesImputer can infer a regular DatetimeIndex frequency and reinsert missing timestamp rows inside the observed range before imputation.
Added optional deterministic calendar/trend features (add_time_features, on by default) that stay observed through contiguous timestamp gaps.
Training subsets that are identical across missingness patterns are fitted once and reused.
Large speedup of the imputation pipeline (about 4.5x on the reference TimeSeriesImputer benchmark) with lower memory usage and unchanged imputation results: default FastRidge models are solved from incrementally accumulated Gram matrices, per-pattern training rows are found from an index of missing positions, normalization statistics and feature-selection scoring are computed from masked sums without full-matrix copies, and lag/lead features are built directly in a preallocated matrix.
Fixed excessive memory retention caused by over-allocated NaN-position buffers.
Removed dead internal helpers and the unused global NaN-position pipeline.

Added scikit-learn transformer compatibility for MultivariateImputer and TimeSeriesImputer.
Ensured TimeSeriesImputer parameter updates propagate to its internal MultivariateImputer.
Added pipeline-focused tests for both imputers.

Documentation overhaul with clearer parameters, expanded how-to content, and a new benchmarks page.
New benchmark and utility scripts, plus runnable helpers for scripted runs.
Expanded benchmark artifacts and example outputs (Titanic and PEMS-Bay assets).
Imputation preprocessing updates (including pre-normalization adjustments).
Estimator defaults updated (ridge intercept default behavior).
Classifier baseline switched to DecisionTree.
Reduced repeated concatenations to improve performance in core workflows.
Timing tests to track performance regressions.
Removed unused imports and refreshed plotting assets/styles used in docs.