  Explaining multivariate time series is a compound challenge, as it requires
identifying important locations in the time series and matching complex
temporal patterns. Although previous saliency-based methods addressed the
challenges, their perturbation may not alleviate the distribution shift issue,
which is inevitable especially in heterogeneous samples. We present ContraLSP,
a locally sparse model that introduces counterfactual samples to build
uninformative perturbations but keeps distribution using contrastive learning.
Furthermore, we incorporate sample-specific sparse gates to generate more
binary-skewed and smooth masks, which easily integrate temporal trends and
select the salient features parsimoniously. Empirical studies on both synthetic
and real-world datasets show that ContraLSP outperforms state-of-the-art
models, demonstrating a substantial improvement in explanation quality for time
series data. The source code is available at
\url{https://github.com/zichuan-liu/ContraLSP}.
