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README
Apache-2.0

简体中文 | English | 日本語



PaddleTS 是一个易用的深度时序建模的Python库,它基于飞桨深度学习框架PaddlePaddle,专注业界领先的深度模型,旨在为领域专家和行业用户提供可扩展的时序建模能力和便捷易用的用户体验。PaddleTS 的主要特性包括:

  • 设计统一数据结构,实现对多样化时序数据的表达,支持单目标与多目标变量,支持多类型协变量
  • 封装基础模型功能,如数据加载、回调设置、损失函数、训练过程控制等公共方法,帮助开发者在新模型开发过程中专注网络结构本身
  • 内置业界领先的深度学习模型,包括NBEATS、NHiTS、LSTNet、TCN、Transformer、DeepAR、Informer等时序预测模型, TS2Vec、CoST等时序表征模型,以及 Autoencoder、VAE、AnomalyTransformer等时序异常检测模型
  • 内置多样化的数据转换算子,支持数据处理与转换,包括缺失值填充、异常值处理、归一化、时间相关的协变量提取等
  • 内置经典的数据分析算子,帮助开发者便捷实现数据探索,包括数据统计量信息及数据摘要等功能
  • 自动模型调优AutoTS,支持多类型HPO(Hyper Parameter Optimization)算法,在多个模型和数据集上展现显著调优效果
  • 第三方机器学习模型及数据转换模块自动集成,支持包括sklearn、pyod等第三方库的时序应用
  • 支持在GPU设备上运行基于PaddlePaddle的时序模型
  • 时序模型集成学习能力

最新动态

  • [2023-09-01] :fire::fire: 飞桨AI套件 PaddleX 发布全新2.0版本, 围绕飞桨模型提供了一站式、全流程、高效率的开发平台,希望助力AI技术快速落地、使人人成为AI Developer!欢迎大家使用和共建。
  • 新增时序分类能力
  • 全新发布6个深度时序模型。 USAD(UnSupervised Anomaly Detection)与MTAD_GAT(Multivariate Time-series Anomaly Detection via Graph Attention Network)异常检测模型, CNN与Inception Time时序分类模型, SCINet(Sample Convolution and Interaction Network)与TFT(Temporal Fusion Transformer)时序预测模型
  • 新发布Paddle Inference支持,已适配时序预测与时序异常检测
  • 新增模型可解释性能力。包括模型无关的可解释性与模型相关的可解释性
  • 新增支持基于表征的聚类与分类

您也可以参考发布说明获取更详尽的更新列表。

未来,更多的高级特性会进一步发布,包括但不限于:

  • 更多时序模型
  • 场景化Pipeline,支持端到端真实场景解决方案

技术交流

飞桨低代码开发工具(PaddleX)—— 面向国内外主流AI硬件的飞桨精选模型一站式开发工具。包含如下核心优势:

  • 【产业高精度模型库】:覆盖10个主流AI任务 40+精选模型,丰富齐全。
  • 【特色模型产线】:提供融合大小模型的特色模型产线,精度更高,效果更好。
  • 【低代码开发模式】:图形化界面支持统一开发范式,便捷高效。
  • 【私有化部署多硬件支持】:适配国内外主流AI硬件,支持本地纯离线使用,满足企业安全保密需要。

PaddleX官网地址:https://aistudio.baidu.com/intro/paddlex

PaddleX官方交流频道:https://aistudio.baidu.com/community/channel/610

关于 PaddleTS

具体来说,PaddleTS 时序库包含以下子模块:

模块 简述
paddlets.datasets 时序数据模块,统一的时序数据结构和预定义的数据处理方法
paddlets.autots 自动超参寻优
paddlets.transform 数据转换模块,提供数据预处理和特征工程相关能力
paddlets.models.forecasting 时序模型模块,基于飞桨深度学习框架PaddlePaddle的时序预测模型
paddlets.models.representation 时序模型模块,基于飞桨深度学习框架PaddlePaddle的时序表征模型
paddlets.models.anomaly 时序模型模块,基于飞桨深度学习框架PaddlePaddle的时序异常检测模型
paddlets.models.classify 时序模型模块,基于飞桨深度学习框架PaddlePaddle的时序分类模型
paddlets.pipeline 建模任务流模块,支持特征工程、模型训练、模型评估的任务流实现
paddlets.metrics 效果评估模块,提供多维度模型评估能力
paddlets.analysis 数据分析模块,提供高效的时序特色数据分析能力
paddlets.ensemble 时序集成学习模块,基于模型集成提供时序预测能力
paddlets.xai 时序模型可解释性模块
paddlets.utils 工具集模块,提供回测等基础功能

安装

前置条件

  • python >= 3.7
  • paddlepaddle >= 2.3

pip 安装 paddlets 命令如下:

pip install paddlets

更多安装方式请参考:环境安装

文档

社区

欢迎大家进入:AI Studio【PaddleX社区频道】进一步交流。地址直达:https://aistudio.baidu.com/community/channel/610

代码发布与贡献

我们非常感谢每一位代码贡献者。如果您发现任何Bug,请随时通过提交issue的方式告知我们。

如果您计划贡献涉及新功能、工具类函数、或者扩展PaddleTS的核心组件相关的代码,请您在提交代码之前先提交issue,并针对此次提交的功能与我们进行讨论。

如果在没有讨论的情况下直接发起的PR请求,可能会导致此次PR请求被拒绝。原因是对于您提交的PR涉及的模块,我们也许希望该模块朝着另一个不同的方向发展。

许可证

PaddleTS 使用Apache风格的许可证, 可参考 LICENSE 文件.

Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. 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简介

PaddleTS 是一个易用的深度时序建模的Python库,它基于飞桨深度学习框架PaddlePaddle,专注业界领先的深度模型,旨在为领域专家和行业用户提供可扩展的时序建模能力和便捷易用的用户体验。 展开 收起
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