v1.2.0

What's Changed

• Add intermediate API's to all models. by @aadyotb in #90
  • Internal changes; should not cause any breaking changes for end users.
  • Implements train(), forecast(), and get_anomaly_score() at the level of the base class for all models. Each of these methods respectively calls an implementation-specific _train(), _forecast(), or _get_anomaly_score().
  • The base classes now include much of what was previously boilerplate code that had to be duplicated for each model (applying pre-processing transforms, converting standard errors to inter-quartile ranges, training post-rules, etc.).
• Fix forecasting bugs when return_prev=True. by @aadyotb in #97

Major Changes Since v1.1.0

• Added intermediate API's to all models.
• Made installation more lightweight.
• Changed Python-Java bridge from jpype to py4j, for improved robustness in multiprocessing settings.
• Various bugfixes.

Full Changelog: v1.1.3...v1.2.0

v1.1.3

What's Changed

• Fix ETS refit params (Issue #78). by @aadyotb in #79
• Allow TimeSeries to include NaN values. by @aadyotb in #85
• Fix n_retrain option in benchmark_forecast.py (Issue #82). by @aadyotb in #86

Full Changelog: v1.1.2...v1.1.3

v1.1.2

What's Changed

• Replace absolute path with a relative one in the model saving/loading procedure by @isenilov in #63
• Combine boosting/bagging trees into one file. by @aadyotb in #68
• Add incremental training option to DAGMM model by @isenilov in #65
• Change default min_likelihood to 1e-16 for BOCPD by @cnll0075 in #71
• Let eval metrics work w/ UnivariateTimeSeries. by @aadyotb in #74
• Implement reconciliation for hierarchical time series. by @aadyotb in #72

Full Changelog: v1.1.1...v1.1.2

v1.1.1

What's Changed

• Explicitly specify int64 for timestamps (not int) to fix a Windows bug (#58)
• Fix bugs with MoE_ForecasterEnsemble (#51), default models (#57), and Prophet holidays (#59)
• Make base installation more lightweight (#61)
• Use py4j instead of jpype for Python-Java bridge (#62)

Full Changelog: v1.1.0...v1.1.1

v1.1.0

What's Changed

• Re-architecture of layered models & ensembles, making AutoML easier to use (#47)
• Added AutoML variants of ETS and Prophet (AutoETS and AutoProphet, similar to the existing AutoSarima)
• Bug fixes related to resampling (#45)
• Improved quality of API docs for model configs

Major Changes Since v1.0.0

• Added change point detection module.
• Re-architecture of layered models & ensembles.
• Expanded AutoML module, with improved ease of use.
• Various bugfixes.

Full Changelog: v1.0.2...v1.1.0

v1.0.2

What's Changed

• Add change point detection module (#41)
• Add more config options to Prophet (#43)

Full Changelog: v1.0.1...v1.0.2

v1.0.1

What's Changed

• Update prophet version (#18)
• Allow sampling granularities that aren't a constant number of seconds, e.g. monthly (#30)
• Fix bugs with AutoSARIMA implementation (#32)
• Make data loading code more robust (#35)
• Fix minor bug in benchmark_anomaly.py (#38)

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

v1.0.0

Initial release.