Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Online
<|reference_start|>Adaptive Conformal Inference for Multi-Step Ahead Time-Series Forecasting Online: 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<|reference_end|>
@article{szabadváry2024adaptive,
    title={Adaptive Conformal Inference for Multi-Step Ahead Time-Series
  Forecasting Online},
    author={Johan Hallberg Szabadv'ary},
    journal={The conference version was published in Proceedings of Machine
  Learning Research, 230:250-263, 2024 (COPA 2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14792},
    primaryClass={stat.ML cs.LG}
}