Fix issues with LayeredModelConfig accessing underlying model attribu…

…tes. (salesforce#104)

* Simplify BOCPD implementation.

* Remove overcomplicated _online_model check.

* Fix LayeredModel access to underlying model params

* Add docstring.

* Have AutoMLMixin inherit from LayeredModel.

* Add feature summary line for change point detect.

* Update version to 1.2.1.

Fix forecasting bugs when return_prev=True. (salesforce#97)

* Fix forecasting bugs when return_prev=True.

* Elaborate on Java install instructions.

Update version to v1.1.3. (salesforce#88)

Implement reconciliation for hierarchical time series. (salesforce#72)

* Fix some AutoSarima bugs.

* Harden models to granularities like MS

* Add RMSPE forecasting eval metric.

* Implement min-trace reconciliation.

* Fix bug for seasonality models on multivar data.

* Add test for minT reconciliation.

* Update docs.

* Fix computation of covariance matrix.

* Update version.

* Add data I/O utils for hierarchical time series.

* Add merlion.utils.data_io to docs.

* Add data I/O test.

Use py4j instead of jpype for Python-Java bridge. (salesforce#62)

* Use py4j instead of jpype for Python-Java bridge.

* Remove test fixtures to handle jpype brittleness.

* Remove unused _convert_point method

* Update plotly install messages.

Co-authored-by: Paul Kassianik <[email protected]>

Re-Architecture of Layered Models & Ensembles (salesforce#47)

* Fix some AutoSarima bugs.

* Harden models to granularities like MS

* Correct import path.

* Fix save/load behavior for layered models.

* Implement generic LayeredModel class.

* More helpful model config __init__ docstrings.

Create a new metaclass which allows model configs to
1. Inherit params from superclass __init__, and have those params
   included in the signature for the subclass __init__.
2. Inherit param docstrings from superclass __init__, which means that
   users don't have to rewrite lots of docstrings for superclass params.

* Various minor fixes.

Also suppresses output from Prophet while the model is training.

* Update gson from 2.8.6 to 2.8.9.

Addresses security risk.

* Order docstring params according to fn signature.

* Fix failing tests.

* Allow None models in LayeredModel.

* Make Merlion default models subclass LayeredModel.

* Allow bubbling of callable attributes in Layers.

* Simplify AutoSARIMA implementation.

* Minor fixes to default model.

* Move LayeredModel to a new file.

* Add dynamic inheritance to LayeredModel's.

* Allow AutoSarima to use a SarimaDetector.

* Add more docstrings & reduce code duplication.

* Add auto-seasonality to AutoSarima.

* Update AutoSARIMA example.

* Update tests to avoid segfault.

* Actually use approx_iter in AutoSARIMA

* Implement __reduce__ for Config & LayeredModel.

This ensures that everything is usable by multiprocessing code.
The use of _original_cls for LayeredModel.__reduce__ ensures that the
right class object is used when attempting to initialize the object, not
a dynamically defined subclass.

* change p value to 0.1

* change default regression method to c in KPSS test

* Better defined periodicity strategies.

* Add distinct AutoProphet model.

* Add AutoETS model.

* Various updates to make serialization work.

- Override setstate/getstate for ensembles
- Use a more refined method to check for unused kwargs in config init

* Remove depth field & fix _save_state() bug.

* Add comment on _original_cls

* Add example for ModelConfigMeta.

* Various AutoProphet bug fixes.

* Fix from_dict() implementations

* Simplify LayeredModel._save_state()

* Move ensemble.models from model to config.

This mirrors the changes to LayeredModel, and it greatly simplifies a
number of implementation details.

* Fix Sphinx errors & handle docstring suffixes.

* Add p-value to SeasonalityConfig.

* Move AutoETS & AutoProphet to models.automl

* Update benchmark_forecast.json

* Implement bubbling for LayeredModel.__setattr__

* Add robustness to None models in serialization.

Co-authored-by: Chenghao Liu <[email protected]>

Remove new .rst files when building docs.

Also treat Sphinx warnings as errors, and add an exit handler to
restore git state if running the build_docs.sh script locally.

Update installation instructions & fix minor bug in benchmark_anomaly…

….py (salesforce#38)

* Use correct delay in benchmark_anomaly (Issue salesforce#37)

* Update install instructions (Issue salesforce#21 discussion)

Prevent build from failing due to empty git stash.

Also try to build docs from PR's in the future.