论文标题
多步进自适应形式异质分段时间序列预测的多步骤的一般框架
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecasting
论文作者
论文摘要
本文介绍了一种称为自适应集合批量多输入多输出的多输出共包式的分位数回归（AENBMIMOCQR}）的新型模型 - 不足算法（AENBMIMOCQR}，该回归（AENBMIMOCQR}，使预测者能够生成多个预测间隔的多步骤的预测间隔，以实现固定的预测范围，以确定的预测范围，我们的方法是无效的。覆盖范围即使数据不可交换，除了在预测范围内在经验上有效，也不会忽略异质性实验，我们证明我们的方法在实验部分中使用的代码和合成数据集都优于其他竞争方法。
This paper introduces a novel model-agnostic algorithm called adaptive ensemble batch multi-input multi-output conformalized quantile regression (AEnbMIMOCQR} that enables forecasters to generate multi-step ahead prediction intervals for a fixed pre-specified miscoverage rate in a distribution-free manner. Our method is grounded on conformal prediction principles, however, it does not require data splitting and provides close to exact coverage even when the data is not exchangeable. Moreover, the resulting prediction intervals, besides being empirically valid along the forecast horizon, do not neglect heteroscedasticity. AEnbMIMOCQR is designed to be robust to distribution shifts, which means that its prediction intervals remain reliable over an unlimited period of time, without entailing retraining or imposing unrealistic strict assumptions on the data-generating process. Through methodically experimentation, we demonstrate that our approach outperforms other competitive methods on both real-world and synthetic datasets. The code used in the experimental part and a tutorial on how to use AEnbMIMOCQR can be found at the following GitHub repository: https://github.com/Quilograma/AEnbMIMOCQR.
