A Flexible Forecasting Stack

Tim Januschowski

doi:10.14778/3685800.3685813

2024-11-08

PRE

Tim Januschowski *

Yuyang Wang

Jan Gasthaus

Syama Sundar Rangapuram

DOI：10.14778/3685800.3685813

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摘要

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预测将时间序列的值外推到未来，这对许多企业和组织优化核心运营至关重要。尽管如此，构建基于机器学习（ML）的预测应用仍具挑战性，原因在于非平稳数据和大数量时间序列。由于不存在单一主导的预测方法，预测系统必须支持广泛的方法，从基于深度学习的方法到基于概率建模的经典方法。我们回顾了在VLDB 2017上发表的关于预测的单一平台早期工作，并描述了我们如何将其演变为一个现代预测堆栈，该堆栈由多个层级组成，支持广泛的预测需求并自动化模型选择等常见任务。该堆栈利用了我们的开源预测库GluonTS和AutoGluon-TimeSeries、可扩展的ML平台SageMaker，并构成了无代码预测解决方案（SageMaker Canvas和Amazon Forecast）的基础，这些解决方案可在Amazon Web Services云中使用。我们分析了该堆栈的预测性能，并讨论了将其用于为云数据库服务DynamoDB、Redshift和Athena配置资源所获得的经验。

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Proceedings of the VLDB Endowment

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3.3

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563

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cmu

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bifold

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materialize

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databricks

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amazon.com

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700

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