用水平分辨率归因解释时间序列预测

中文摘要 AI 辅助

时间序列（TS）模型解释方面的最新进展已经产生了识别预测所依赖的过去值的方法。然而，大多数现有方法返回一个单一的重要性向量，假设每个预测步骤都依赖于相同的过去值。在本文中，我们表明这一假设不成立，因为不同的预测步骤依赖于不同的过去值。基于这一观察，我们提出了水平分辨率解释（HRX），它为解释添加了一个水平轴，使得每个预测步骤都有自己的重要性图。HRX是一个简单而有效的即插即用框架，包含三个组件：1）一个估计器，它从任何可微的预测器中读取这些图，而无需修改TS骨干网络；2）一个评估协议，通过测量当重要性图排名最高的输入被移除时单个预测步骤的变化来验证水平轴；3）一个排名标准，预先预测该轴是否值得在给定的TS上解析。我们进一步表明，这种逐步依赖性具有低维性，因为所有步骤的解释是由几个共享图构建的，其数量不随预测长度增长。在各种骨干网络和数据集上的大量实验表明，改进来自水平轴，并且对先前解释方法的估计器也成立。代码可在该https URL获取。

英文摘要

Recent advances in explaining time series (TS) models have produced methods that identify which past values a prediction depends on. However, most existing methods return a single importance vector, assuming that every predicted step depends on the same past values. In this paper, we show that this assumption does not hold, as different forecast steps depend on different past values. Motivated by this observation, we propose Horizon-Resolved eXplanation (HRX), which adds a horizon axis to the explanation, so that every forecast step receives its own importance map. HRX is a simple yet effective plug-in framework with three components: 1) an estimator that reads these maps out of any differentiable forecaster without modifying the TS backbone, 2) an evaluation protocol that validates the horizon axis by measuring how much a single forecast step changes when the inputs an importance map ranks highest are removed, and 3) a rank criterion that predicts in advance whether the axis is worth resolving on a given TS. We further show that this step-wise dependence is low-dimensional, as the explanations of all steps are built from a few shared maps whose number does not grow with the forecast length. Extensive experiments across various backbones and datasets show that the improvement comes from the horizon axis and holds for estimators of previous explanation methods. Code is available at https://github.com/seunghan96/HRX.

发表机构

• LG AI Research（LG AI研究院）

机构由 AI 辅助整理，请以论文原文为准。