Conformal Predictive Simulations for Univariate Time Series

Thierry Moudiki
Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 266:751-752, 2025.

Abstract

Uncertainty quantification is useful because it allows, among other things, for the as- sessment of impact of alternative, hypothetical scenarios on business metrics of interest. For example, in the context of electricity load forecasting, uncertainty quantification can help in assessing the impact of a drop in temperature on electricity demand, and taking appropriate measures to avoid blackouts. In financial forecasting, uncertainty quantification can help in assessing the impact of an increase in stock market on a portfolio, and taking appropriate measures to avoid large losses. Another application, in insurance, is the calculation of capital requirements in extremely adverse situations. In this context, despite having been available for decades, Conformal Prediction (CP) is becoming more and more popular, and a gold standard technique. This study proposes a revisited approach to uncertainty quantification for univariate time series forecasting, that can be adapted to multivariate time series forecasting. The approach adapts split conformal prediction, usually applied to tabular data but never to sequential data, to sequential data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v266-moudiki25a, title = {Conformal Predictive Simulations for Univariate Time Series}, author = {Moudiki, Thierry}, booktitle = {Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {751--752}, year = {2025}, editor = {Nguyen, Khuong An and Luo, Zhiyuan and Papadopoulos, Harris and Löfström, Tuwe and Carlsson, Lars and Boström, Henrik}, volume = {266}, series = {Proceedings of Machine Learning Research}, month = {10--12 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v266/main/assets/moudiki25a/moudiki25a.pdf}, url = {https://proceedings.mlr.press/v266/moudiki25a.html}, abstract = {Uncertainty quantification is useful because it allows, among other things, for the as- sessment of impact of alternative, hypothetical scenarios on business metrics of interest. For example, in the context of electricity load forecasting, uncertainty quantification can help in assessing the impact of a drop in temperature on electricity demand, and taking appropriate measures to avoid blackouts. In financial forecasting, uncertainty quantification can help in assessing the impact of an increase in stock market on a portfolio, and taking appropriate measures to avoid large losses. Another application, in insurance, is the calculation of capital requirements in extremely adverse situations. In this context, despite having been available for decades, Conformal Prediction (CP) is becoming more and more popular, and a gold standard technique. This study proposes a revisited approach to uncertainty quantification for univariate time series forecasting, that can be adapted to multivariate time series forecasting. The approach adapts split conformal prediction, usually applied to tabular data but never to sequential data, to sequential data.} }
Endnote
%0 Conference Paper %T Conformal Predictive Simulations for Univariate Time Series %A Thierry Moudiki %B Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2025 %E Khuong An Nguyen %E Zhiyuan Luo %E Harris Papadopoulos %E Tuwe Löfström %E Lars Carlsson %E Henrik Boström %F pmlr-v266-moudiki25a %I PMLR %P 751--752 %U https://proceedings.mlr.press/v266/moudiki25a.html %V 266 %X Uncertainty quantification is useful because it allows, among other things, for the as- sessment of impact of alternative, hypothetical scenarios on business metrics of interest. For example, in the context of electricity load forecasting, uncertainty quantification can help in assessing the impact of a drop in temperature on electricity demand, and taking appropriate measures to avoid blackouts. In financial forecasting, uncertainty quantification can help in assessing the impact of an increase in stock market on a portfolio, and taking appropriate measures to avoid large losses. Another application, in insurance, is the calculation of capital requirements in extremely adverse situations. In this context, despite having been available for decades, Conformal Prediction (CP) is becoming more and more popular, and a gold standard technique. This study proposes a revisited approach to uncertainty quantification for univariate time series forecasting, that can be adapted to multivariate time series forecasting. The approach adapts split conformal prediction, usually applied to tabular data but never to sequential data, to sequential data.
APA
Moudiki, T.. (2025). Conformal Predictive Simulations for Univariate Time Series. Proceedings of the Fourteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 266:751-752 Available from https://proceedings.mlr.press/v266/moudiki25a.html.

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