标题： KnowIt：深度时间序列建模与解释 显示英文标题

标题： KnowIt: Deep Time Series Modeling and Interpretation

摘要： KnowIt（时间序列数据中的知识发现）是一个灵活的框架，用于构建深度时间序列模型并对其进行解释。 它被实现为一个Python工具包，源代码和文档可从https://must-deep-learning.github.io/KnowIt获取。 它对任务规范的假设最少，并通过定义明确的接口将数据集、深度神经网络架构和可解释性技术的定义分离开来。 这确保了在保持对用户自身时间序列数据的不同方面的实时建模和解释的同时，易于导入新的数据集、自定义架构以及定义不同的可解释性范式。 KnowIt旨在提供一个环境，让用户通过构建强大的深度学习模型并解释其行为，在自己的复杂时间序列数据上进行知识发现。 随着持续的发展、合作和应用，我们的目标是使这成为一个推动这一未充分研究领域发展的平台，并为深度时间序列建模提供一个可信的工具。 显示英文摘要

摘要： KnowIt (Knowledge discovery in time series data) is a flexible framework for building deep time series models and interpreting them. It is implemented as a Python toolkit, with source code and documentation available from https://must-deep-learning.github.io/KnowIt. It imposes minimal assumptions about task specifications and decouples the definition of dataset, deep neural network architecture, and interpretability technique through well defined interfaces. This ensures the ease of importing new datasets, custom architectures, and the definition of different interpretability paradigms while maintaining on-the-fly modeling and interpretation of different aspects of a user's own time series data. KnowIt aims to provide an environment where users can perform knowledge discovery on their own complex time series data through building powerful deep learning models and explaining their behavior. With ongoing development, collaboration and application our goal is to make this a platform to progress this underexplored field and produce a trusted tool for deep time series modeling.