TimeREISE

This repository provides all code necessary to reproduce the results reported in our paper TimeREISE: Time-series Randomized Evolving Input Sample Explanation[Sensors][arXiv].

Abstract: Deep neural networks are one of the most successful classifiers across different domains. However, their use is limited in safety-critical areas due to their limitations concerning interpretability. The research field of explainable artificial intelligence addresses this problem. However, most interpretability methods align to the imaging modality by design. The paper introduces TimeREISE, a model agnostic attribution method that shows success in the context of time series classification. The method applies perturbations to the input and considers different attribution map characteristics such as the granularity and density of an attribution map. The approach demonstrates superior performance compared to existing methods concerning different well-established measurements. TimeREISE shows impressive results in the deletion and insertion test, Infidelity, and Sensitivity. Concerning the continuity of an explanation, it showed superior performance while preserving the correctness of the attribution map. Additional sanity checks prove the correctness of the approach and its dependency on the model parameters. TimeREISE scales well with an increasing number of channels and timesteps. TimeREISE applies to any time series classification network and does not rely on prior data knowledge. TimeREISE is suited for any usecase independent of dataset characteristics such as sequence length, channel number, and number of classes.

Requirements

An appropriate Python environment can be set up using the src/requirements.txt file provided in the repo. The respective datasets can be downloaded from the UEA & UCR Time Series Classification Repository and should be placed in the data/ folder.

Basic Usage

Results can be reproduced by running the corresponding bash scripts located in the subfolders of src/bash_scripts/ as outlined in the table below. Models are saved in models/ and resulting evaluation files are placed under results/.

Script - Description	File
Script 1 - Train the baseline models	execute_baseline.sh
Script 2 - Compute the attributions	execute_attribution.sh
Script 3 - Compute the metrics	execute_metrics.sh

Advanced Usage

Additional notebooks to compute figures and execute the approach in a notebook can be found in src/notebooks.

Notebook - Description	File
Notebook 1 - Train baseline, compute attribution	classification.ipynb
Notebook 2 - Extract runtime from logs	runtime_extract.ipynb
Notebook 3 - Compute sanity of TimeREISE	sanity_checker.ipynb
Notebook 4 - Produces paper plots	paper_plots.ipynb

Citation

Please consider citing our associated paper:

    @Article{s22114084,
        AUTHOR = {Mercier, Dominique and Dengel, Andreas and Ahmed, Sheraz},
        TITLE = {TimeREISE: Time Series Randomized Evolving Input Sample Explanation},
        JOURNAL = {Sensors},
        VOLUME = {22},
        YEAR = {2022},
        NUMBER = {11},
        ARTICLE-NUMBER = {4084},
        URL = {https://www.mdpi.com/1424-8220/22/11/4084},
        ISSN = {1424-8220},
        DOI = {10.3390/s22114084}
    }