Hotfix

• [FIX] Use Numpy bool_ instead of Python bool in eagerly compiled functions by @elephaint in #315

Full Changelog: v1.0.0...v1.0.1

New features

• [FEAT] Polars support in #305
• [FEAT] Evaluation to utils in #311

Breaking changes

As of v1.0.0, HierarchicalForecast no longer supports the unique_id as index. Users may have to perform a .reset_index() when using a Pandas DataFrame that has the unique_id still as index. The old behavior has been deprecated throughout the entire Nixtlaverse, so it may be wise to update all Nixtla packages to ensure the same consistent behavior is observed everywhere.

Full Changelog: v0.4.3...v1.0.0

New Features

• [FEAT] Sparse middle-out reconciliation via MiddleOutSparse @christophertitchen (#281)
• [FEAT] Add support for exogenous variables in utils.aggregate @KuriaMaingi (#297)
• [FEAT] Efficient Schafer-Strimmer for MinT @elephaint (#280)
• [FEAT] Improve residuals-based reconciliation stability and faster ma.cov @elephaint (#295)

Dependencies

• As of v0.4.3, hierarchicalforecast no longer officially supports Python 3.8, which is EOL.

New Features

• Add sparse top-down reconciliation via TopDownSparse @christopher-titchen (#277)
• Decrease wall time of _get_PW_matrices for BottomUp and BottomUpSparse @christopher-titchen (#276)
• Efficient MinTrace (ols/wls_var/wls_struct/mint_cov/mint_shrink) @elephaint (#264)

Documentation

• Create CODE_OF_CONDUCT.md @tracykteal (#267)
• Fix evaluate argument in readme @jmoralez (#257)
• Update ml frameworks example @jmoralez (#254)
• Add step to trigger mintlify workflow @rpmccarter (#259)

Dependencies

• Remove numpy pin @DManowitz (#272)

Enhancement

• Warn when num_threads is not used in MinTrace @jmoralez (#273)

Bug Fixes

• keep only observed combinations in aggregate @jmoralez (#242)

Documentation

• Fix parsing errors and add mint.json @hahnbeelee (#243)

Enhancement

• support parallel MinTrace optimization @jmoralez (#249)

New Features

• Sparse Reconciliation @mcsqr (#210)
• [FEAT] Probabilistic Forecasting Util Functions @dluuo (#195)
• [FEAT] NeuralForecast Compatibility and Example Notebook @dluuo (#188)

Bug Fixes

• fix aggregate function @jmoralez (#232)
• [FIX] Aggregate unbalanced datasets @FedericoGarza (#190)
• Fix assignment to unbound variable @nickto (#187)

Documentation

• [Doc] Updated FavoritaComplete evaluation @kdgutier (#220)
• [Doc] Added baseline version detail for replicability @kdgutier (#218)
• [Doc] Added HierE2E Favorita baseline @kdgutier (#217)
• [Doc] aggregate showdoc + external reconciliation tutorials' improvements @kdgutier (#214)
• [Doc] First iteration of HierE2E baseline execution + Documentation detail improvements @kdgutier (#212)
• [Doc] Added baseline experiments and minor protection to Normality reconciler @kdgutier (#203)
• [FEAT] HierarchicalForecast With GluonTS Example Notebook @dluuo (#200)
• [Doc] Fix intro installation typo @kdgutier (#193)

Enhancement

• Fixes for large datasets @mcsqr (#229)
• Rename MSSE into RelMSE, add new implementation of MSSE @nickto (#185)
• [FEAT] Core Numeric Type and Null Protections @dluuo (#181)

Computational Efficiency Improvements

• New aggregate function that generates the hierarchical time series and the aggregation constraints matrix. Improve from $O((N_{a}+N_{b})^{2}log(N_{a}+N_{b}))$ to $O((N_{a}+N_{b})$.
• Vectorization of the creation of probabilistic prediction levels, before done in for loops now performed in a single vectorized numpy call.

Evaluation Utilities

• Added scaled continuous ranked probability scores (sCRPS).
• Added mean scaled squared errors (MSSE).
• Added energy score metric.
• Added random sampling outputs to probabilistic reconcilers.
• Added core.bootstrap_reconcile method to apply over different random seeds the reconcilers and generate standard deviations.

Refactorization of the HierarchicalForecast classes

• Overall improvement of the core.reconciliation method.
• Decoupled the probabilistic reconciler classes from the mean reconciler classes.
• Decoupled fit protections from reconciliation.
• Reconciler's inputs now mostly receive mostly numpy arrays.
• Simplified and deprecated dependencies.

Documentation Improvements

• Installation guide.
• New introduction tutorial with minimal, intuitive example.
• Tutorial on evaluation of reconciliation probabilistic reconciliation baselines.

New Collaborators and HierarchicalForecast Paper

• We started a fruitful collaboration with Souhaib Ben Taieb
  and Shanika Wickramasuriya.
• We submitted the HierarchicalForecast library paper to the Journal of Machine Learning Research.

What's Changed

• [FEAT] Ignore jupyter notebooks as part of languages in #120
• [FEAT] Factorizing reverse_sigmah from HierarchicalReconciliation in #121
• [FEAT] Decoupling _reconcile, from _get_PW_matrices. in #123
• [FEAT] PW initialization in #124
• Prob Reconciler's tests location in #125
• Core Refactorization + Reconcilers.fit in #128
• CircleCI in #129
• Shared HReconciler + predict method in #131
• [FEAT] Reconciler's sample method in #133
• [FEAT] CRPS, MSSE and Energy Score metrics in #134
• time tracking utils in #135
• [FEAT] Faster creation of ProbReconciler's ordered levels in #137
• [FIX] Matplotlib and numba errors in #142
• [FIX] Circle ci integration in #141
• [BUG] PERMBU unique_id order and num_samples in #143
• [Bug] Fixed S_df categorical index ordering in #145
• [FEAT] seed/num_samples usage possibility + MSSE evaluation example in #147
• [FEAT] Faster aggregate function + Gaussian Log Score in #150
• [FIX] Documentation + Update bib reference in #156
• light improvements to readme in #157
• [FIX] Use micromamba instead of miniconda (CI) in #167
• [BUG] Added level domain protection for normality and permbu methods in #166
• Level domain protection in #169
• Omit expensive linear algebra when not necessary in MinTrace in #171
• [FIX] Add correct github link in #173
• [DOCS] Improved index, intro, quick start, and geographical forecasts in #175

New Contributors

• @melopeo made their first contribution in #157
• @mcsqr made their first contribution in #171
• @cchallu made their first contribution in #175

Full Changelog: v0.2.1...v0.3.0

What's Changed

• Introduction tutorial in #102
• [FIX] Docs source links in #107
• [FIX] General plot_hierarchical_predictions_gap in #106
• Doc: Updated ReadMe in #111
• FEAT: add installation guide in #114
• FEAT: Documentation Outline in #112
• [FIX] Add correct link to StatsForecast in #115
• [FIX] Deprecate mycolorpy dependency in #116
• [FEAT] Add conda badge to readme in #117

Full Changelog: v0.2.0...v0.2.1

What's Changed

• MinTrace's protection to Schafer-Strimmer covariance and eliminated statsmodels dependency in #97

Full Changelog: v0.1.3...v0.2.0

What's Changed

• Utils documentation title change + H. aggregation gap plot in #71
• PERMBU in #73
• [FEAT] Non-negative reconciliation in #78
• [FIX] Examples numbering in #84
• [FEAT,BREAKING CHANGE] Add PERMBU integration to HierarchicalReconciliation class in #83
• [FEAT] Add test same series Y and S in #94

Full Changelog: v0.1.2...v0.1.3