Title: SeqSHAP: Subsequence Level Shapley Value Explanations for Sequential Predictions. Abstract: With the increasing demands of interpretability in real-world applications, various methods for explainable artificial intelligence (XAI) have been proposed. However, most of them overlook the interpretability in sequential scenarios, which have a wide range of applications, e.g., online transactions and sequential recommendations. In this paper, we propose a Shapley value based explainer named SeqSHAP to explain the model predictions in sequential scenarios. Compared to existing methods, SeqSHAP provides more intuitive explanations at a subsequence level, which explicitly models the effect of contextual information among the related elements in a sequence. We propose to calculate subsequence-level feature attributions instead of element-wise attributions to utilize the information embedded in sequence structure, and provide a distribution-based segmentation method to obtain reasonable subsequences. Extensive experiments on two online transaction datasets from a real-world e-commerce platform show that the proposed method could provide valid and reliable explanations for sequential predictions.