Conformal prediction interval for dynamic time-series
Conformal prediction interval for dynamic time-series
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发表时间：
2020-10
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通讯作者：
Chen Xu;Yao Xie
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作者：
Chen Xu;Yao Xie
我们开发了一种基于保角推理的时间序列预测区间的方法，称为\Verb| ENPI|它环绕任何总体估计器来构建顺序预测区间。动词|ENPI|与共形预报（CP）框架密切相关，但不需要数据交换。从理论上讲，这些区间达到有限样本，近似有效的平均覆盖广泛的回归函数和时间序列的强烈混合随机误差。计算，动词|ENPI|不需要训练多个集成估计器;它有效地围绕已经训练的集成估计器进行操作。一般来说，\Verb| ENPI|易于实现，可扩展到顺序地产生任意多个预测区间，并且非常适合于广泛的回归函数。我们进行了大量的模拟和真实数据分析，以证明其有效性。
We develop a method to build distribution-free prediction intervals for time-series based on conformal inference, called \Verb|EnPI| that wraps around any ensemble estimator to construct sequential prediction intervals. \Verb|EnPI| is closely related to the conformal prediction (CP) framework but does not require data exchangeability. Theoretically, these intervals attain finite-sample, approximately valid average coverage for broad classes of regression functions and time-series with strongly mixing stochastic errors. Computationally, \Verb|EnPI| requires no training of multiple ensemble estimators; it efficiently operates around an already trained ensemble estimator. In general, \Verb|EnPI| is easy to implement, scalable to producing arbitrarily many prediction intervals sequentially, and well-suited to a wide range of regression functions. We perform extensive simulations and real-data analyses to demonstrate its effectiveness.