Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Online

• URL: http://arxiv.org/abs/2409.14792v1
• Date: Mon, 23 Sep 2024 08:07:49 GMT
• Title: Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Online
• Authors: Johan Hallberg Szabadváry,
• Abstract summary: We propose an adaptation of the adaptive conformal inference algorithm to achieve finite-sample coverage guarantees. Our multi-step ahead ACI procedure inherits these guarantees at each prediction step, as well as for the overall error rate.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The aim of this paper is to propose an adaptation of the well known adaptive conformal inference (ACI) algorithm to achieve finite-sample coverage guarantees in multi-step ahead time-series forecasting in the online setting. ACI dynamically adjusts significance levels, and comes with finite-sample guarantees on coverage, even for non-exchangeable data. Our multi-step ahead ACI procedure inherits these guarantees at each prediction step, as well as for the overall error rate. The multi-step ahead ACI algorithm can be used with different target error and learning rates at different prediction steps, which is illustrated in our numerical examples, where we employ a version of the confromalised ridge regression algorithm, adapted to multi-input multi-output forecasting. The examples serve to show how the method works in practice, illustrating the effect of variable target error and learning rates for different prediction steps, which suggests that a balance may be struck between efficiency (interval width) and coverage.t

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