Explaining Time Series via Contrastive and Locally Sparse Perturbations

• URL: http://arxiv.org/abs/2401.08552v2
• Date: Mon, 29 Jan 2024 04:44:46 GMT
• Title: Explaining Time Series via Contrastive and Locally Sparse Perturbations
• Authors: Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen
• Abstract summary: ContraLSP is a sparse model that introduces counterfactual samples to build uninformative perturbations but keeps distribution using contrastive learning. Empirical studies on both synthetic and real-world datasets show that ContraLSP outperforms state-of-the-art models.
• Score: 45.055327583283315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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