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prepare for 1.15.0 release (#1091)
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CHANGELOG.md

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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## Unreleased
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### Added
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- `RMSE` metric & `rmse` functional metric ([#1051](https://github.com/tinkoff-ai/etna/pull/1051))
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- `MaxDeviation` metric & `max_deviation` functional metric ([#1061](https://github.com/tinkoff-ai/etna/pull/1061))
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- Add saving/loading for transforms, models, pipelines, ensembles; tutorial for saving/loading ([#1068](https://github.com/tinkoff-ai/etna/pull/1068))
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- Add hierarchical time series support([#1083](https://github.com/tinkoff-ai/etna/pull/1083))
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- Add `WAPE` metric & `wape` functional metric ([#1085](https://github.com/tinkoff-ai/etna/pull/1085))
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### Changed
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### Fixed
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## [1.15.0] - 2023-01-31
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### Added
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- `RMSE` metric & `rmse` functional metric ([#1051](https://github.com/tinkoff-ai/etna/pull/1051))
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- `MaxDeviation` metric & `max_deviation` functional metric ([#1061](https://github.com/tinkoff-ai/etna/pull/1061))
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- Add saving/loading for transforms, models, pipelines, ensembles; tutorial for saving/loading ([#1068](https://github.com/tinkoff-ai/etna/pull/1068))
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- Add hierarchical time series support([#1083](https://github.com/tinkoff-ai/etna/pull/1083))
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- Add `WAPE` metric & `wape` functional metric ([#1085](https://github.com/tinkoff-ai/etna/pull/1085))
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### Fixed
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- Missed kwargs in TFT init([#1078](https://github.com/tinkoff-ai/etna/pull/1078))
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## [1.14.0] - 2022-12-16
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### Added
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- Add python 3.10 support ([#1005](https://github.com/tinkoff-ai/etna/pull/1005))

pyproject.toml

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[tool.poetry]
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name = "etna"
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version = "1.14.0"
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version = "1.15.0"
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repository = "https://github.com/tinkoff-ai/etna"
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readme = "README.md"
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description = "ETNA is the first python open source framework of Tinkoff.ru AI Center. It is designed to make working with time series simple, productive, and fun."

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