Exact and robust conformal inference methods for predictive machine learning with dependent data
Kaspar Wüthrich () and
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Yinchu Zhu: Institute for Fiscal Studies
No CWP16/18, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies
We extend conformal inference to general settings that allow for time series data. Our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme. As a result, the proposed method retains the exact, model-free validity when the data are i.i.d. or more generally exchangeable, similar to usual conformal inference methods. When exchangeability fails, as is the case for common time series data, the proposed approach is approximately valid under weak assumptions on the conformity score.
Keywords: Conformal inference; permutation and randomization; dependent data; groups (search for similar items in EconPapers)
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