TimeSeg: An Information-Theoretic Segment-Level Explainer for Time-Series Predictions

Figure of Overall Explanation Process

Explaining predictions of black-box time-series models remains a challenging problem due to the dynamically evolving patterns within individual sequences and their complex temporal dependencies. Unfortunately, existing explanation methods largely focus on point-wise explanations, which fail to capture broader temporal context, while methods that attempt to highlight interpretable temporal patterns (Eq. achieved by incorporating a regularizer or fixed-length patches) often lack principled definitions of meaningful segments. This limitation frequently leads to fragmented and confusing explanations for end users. As such, the notion of segment-level explanations has remained underexplored, with little consensus on what constitutes an interpretable segment or how such segments should be identified. To bridge this gap, we define segment-level explanation for black-box time-series models as the task of selecting contiguous subsequences that maximize their joint mutual information with the target prediction. Building on this formulation, we propose TimeSeg, a novel information-theoretic framework that employs reinforcement learning to sequentially identify predictive temporal segments at a per-instance level. By doing so, TimeSeg produces segment-wise explanations that capture holistic temporal patterns rather than fragmented points, providing class-predictive patterns in a human-interpretable manner. Extensive experiments on both synthetic and real‑world datasets demonstrate that TimeSeg produces more coherent and human-understandable explanations, while achieving performance that matches or surpasses existing methods on downstream tasks using the identified segments.

Quick Start

1) Create and activate a conda env (Python 3.9)

conda create --name time-segment python==3.9
conda activate time-segment

2) Install python dependencies

pip install -r requirements.txt

git clone https://github.com/TimeSynth/TimeSynth.git
cd TimeSynth
python setup.py install
cd ..

3) Pre-train Black-box Model

bash ./scripts/blackbox_train.sh

This will pre-train the black-box with default settings. The trained checkpoint is saved under ./model_ckpt/{dataset}/ (e.g., ./model_ckpt/MITECG/) and will be used automatically by the main pipeline.

4) Run TimeSeg (train the explainer)

bash ./scripts/main.sh

This command trains the TimeSeg explainer (policy/value networks) and automatically runs the test step at the end.