v0.4.3

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.

v0.4.2

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)

v0.4.1

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)

v0.4.0

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)

v0.3.0

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

v0.2.1

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

v0.2.0

What's Changed

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

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

v0.1.3

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

v0.1.2

What's Changed

• [FIX] Methods' name in #67

Full Changelog: v0.1.1...v0.1.2

v0.1.1

What's Changed

• Improved documentation, contribution instructions and .gitignore in #57
• Intro paragraph for documentation, tutorial titles, gitignore protect… in #60
• Fixed missing documentation plots, working README example in #61
• [FIX] Plot single-valued time series in #64
• [FIX] h=1 evaluation in #63
• [FIX] Make Y_df optional for the reconcile method in #65

Full Changelog: v0.1.0...v0.1.1