KnowIt: Deep Time Series Modeling and Interpretation

TL;DR

KnowIt is a flexible Python toolkit for deep time series modeling and interpretation, enabling users to easily build, customize, and interpret models for complex time series data.

Contribution

It introduces a decoupled, modular framework for deep time series modeling and interpretation, facilitating easy integration of datasets, architectures, and interpretability methods.

Findings

Provides on-the-fly modeling and interpretation capabilities.

Supports diverse datasets and custom architectures.

Aims to foster knowledge discovery in complex time series data.

Abstract

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.