LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data
LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data
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LIMREF：本地可解释模型不可知的基于规则的预测解释，并应用于电力智能电表数据
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
C. Bergmeir
中科院分区：
文献类型：
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作者：
Dilini Sewwandi Rajapaksha;C. Bergmeir
准确的电力需求预测在可持续电力系统中起着关键作用。为了更好地做出决策，特别是针对最终用户的需求灵活性，不仅需要提供准确的预测，还需要提供可理解和可操作的预测。为了提供准确的预测，跨时间序列训练的全局预测模型（GFM）最近在许多需求预测竞赛和实际应用中显示出上级单变量预测方法的结果。我们的目标是填补全球预测方法的准确性和可解释性之间的差距。
为了解释全球模型预测，我们提出了本地可解释模型不可知的基于规则的预测解释（LIMREF），这是一个本地解释器框架，为特定的预测产生k-最优的影响规则，考虑全球预测模型作为一个黑盒模型，在模型不可知的方式。它提供了不同类型的规则，解释了全局模型的预测和反事实规则，为潜在的变化提供了可操作的见解，以获得给定实例的不同输出。我们使用大规模的电力需求数据集进行实验，这些数据集具有温度和日历效应等外生特征。在这里，我们评估质量的解释所产生的LIMREF框架在定性和定量方面，如准确性，保真度和可理解性，并基准对其他本地解释者。
Accurate electricity demand forecasts play a key role in sustainable power systems. To enable better decision-making especially for demand flexibility of the end-user, it is necessary to provide not only accurate but also understandable and actionable forecasts. To provide accurate forecasts Global Forecasting Models (GFM) that are trained across time series have shown superior results in many demand forecasting competitions and real-world applications recently, compared with univariate forecasting approaches. We aim to fill the gap between the accuracy and the interpretability in global forecasting approaches.
In order to explain the global model forecasts, we propose Local Interpretable Model-agnostic Rule-based Explanations for Forecasting (LIMREF), which is a local explainer framework that produces k-optimal impact rules for a particular forecast, considering the global forecasting model as a black-box model, in a model-agnostic way. It provides different types of rules which explain the forecast of the global model and the counterfactual rules, which provide actionable insights for potential changes to obtain different outputs for given instances. We conduct experiments using a large-scale electricity demand dataset with exogenous features such as temperature and calendar effects. Here, we evaluate the quality of the explanations produced by the LIMREF framework in terms of both qualitative and quantitative aspects such as accuracy, fidelity and comprehensibility, and benchmark those against other local explainers.
影响因子：
5.8
作者：
Simon N;Friedman J;Hastie T;Tibshirani R
通讯作者：
Tibshirani R
影响因子：
56.9
作者：
Perozo, E;Cortes, DM;Cuello, LG
通讯作者：
Cuello, LG