We introduce the Deep Convolutional Interpreter for Time Series (DCIts), a deep-learning architecture for nonlinear multivariate time series that provides sample-specific, locally interpretable descriptions of the underlying interaction structure. Unlike standard black-box forecasters, DCIts learns a time- and lag-dependent transition tensor explicitly factorized into two components: a Focuser, which selects relevant source series and time lags via a sparse masking mechanism, and a Modeler, which assigns signed coefficients to these selected interactions. This decomposition yields a local lag-adjacency structure and signed source-lag contributions for every forecast instance, enabling direct inspection of effective connectivity; when higher-order branches are activated, the same framework yields order-resolved elementwise polynomial contributions. Architecturally, DCIts uses a diverse bank of convolutional filters to capture temporal and cross-variable dependencies, which are mapped through a bottleneck network to the transition tensor. On controlled benchmark datasets with a known interaction structure, we demonstrate that DCIts achieves competitive forecasting error relative to a strong interpretable baseline while recovering stable, signed, lag-resolved interaction patterns. The framework thus prioritizes intrinsic interpretability, using forecasting accuracy as a faithfulness constraint rather than the sole objective.

翻译：我们引入了深度卷积时间序列解释器（DCIts），这是一种针对非线性多元时间序列的深度学习架构，能够提供样本特定、局部可解释的底层交互结构描述。与标准黑箱预测器不同，DCIts学习一个依赖于时间和滞后的转移张量，该张量明确分解为两个组件：聚焦器（Focuser）通过稀疏掩码机制选择相关源序列和时间滞后，建模器（Modeler）为这些选中的交互分配带符号系数。这种分解为每个预测实例生成局部滞后-邻接结构以及带符号的源-滞后贡献，从而可以直接检查有效连接；当高阶分支被激活时，同一框架可生成按阶解析的逐元素多项式贡献。在架构上，DCIts使用多样化的卷积核池来捕捉时间依赖和跨变量依赖，并通过瓶颈网络映射到转移张量。在具有已知交互结构的受控基准数据集上，我们证明DCIts在预测误差上相对于强可解释基线具有竞争力，同时能够恢复稳定、带符号且滞后解析的交互模式。因此，该框架优先考虑内在可解释性，将预测准确性作为保真度约束而非唯一目标。