让复杂时间序列数据建模与解释更灵活高效
KnowIt: Deep Time Series Modeling and Interpretation
- 通过解耦数据、模型与可解释性接口，支持自由组合
- 无需预设任务，可实时构建并解释自定义时间序列模型
- 适合需要深入理解时序数据行为的研究者和工程师
KnowIt（时间序列数据中的知识发现）是一个灵活的深度时间序列建模与解释框架，以 Python 工具包形式实现，源代码与文档可在 https://must-deep-learning.github.io/KnowIt 获取。该框架对任务设定假设极少，通过明确定义的接口，将数据集、深度神经网络架构与可解释性技术分离开来。这使得用户能轻松导入新数据集、自定义网络结构，并定义不同可解释性范式，同时实现对用户自身时间序列数据的即时建模与多维度解释。KnowIt 旨在提供一个环境，使用户可通过构建强大深度学习模型并解释其行为，开展针对复杂时间序列数据的知识发现。随着持续开发、协作与应用，目标是将其打造为推动这一未充分探索领域的平台，并成为可信的深度时间序列建模工具。
原文摘要 · Abstract (English)
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.
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