Learning Interpretable Differentiable Logic Networks for Time-Series Classification
This work extends DLNs to time-series classification, offering interpretable models for domains requiring transparency, though it is incremental as it adapts existing methods to a new data type.
The authors applied differentiable logic networks (DLNs) to univariate time-series classification for the first time, using feature-based representations from Catch22 and TSFresh, and found that DLNs maintained competitive accuracy, low inference cost, and interpretability on 51 benchmarks.
Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational efficiency. In this work, we apply DLNs to the domain of TSC for the first time, focusing on univariate datasets. To enable DLN application in this context, we adopt feature-based representations relying on Catch22 and TSFresh, converting sequential time series into vectorized forms suitable for DLN classification. Unlike prior DLN studies that fix the training configuration and vary various settings in isolation via ablation, we integrate all such configurations into the hyperparameter search space, enabling the search process to select jointly optimal settings. We then analyze the distribution of selected configurations to better understand DLN training dynamics. We evaluate our approach on 51 publicly available univariate TSC benchmarks. The results confirm that classification DLNs maintain their core strengths in this new domain: they deliver competitive accuracy, retain low inference cost, and provide transparent, interpretable decision logic, thus aligning well with previous DLN findings in the realm of tabular classification and regression tasks.