Conformal Prediction for Time Series with Modern Hopfield Networks

Andreas Auer; Martin Gauch; Daniel Klotz; Sepp Hochreiter

Conformal Prediction for Time Series with Modern Hopfield Networks

Andreas Auer
, Martin Gauch
, Daniel Klotz
, Sepp Hochreiter

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains. However, they are difficult to apply to time series as the autocorrelative structure of time series violates basic assumptions required by conformal prediction. We propose HopCPT, a novel conformal prediction approach for time series that not only copes with temporal structures but leverages them. We show that our approach is theoretically well justified for time series where temporal dependencies are present. In experiments, we demonstrate that our new approach outperforms state-of-the-art conformal prediction methods on multiple real-world time series datasets from four different domains.

Originalsprache	Englisch
Titel	Conference Neural Information Processing Systems Foundation (NeurIPS 2023)
Seitenumfang	48
Publikationsstatus	Veröffentlicht - 2023

Wissenschaftszweige

305907 Medizinische Statistik
202017 Embedded Systems
202036 Sensorik
101004 Biomathematik
101014 Numerische Mathematik
101015 Operations Research
101016 Optimierung
101017 Spieltheorie
101018 Statistik
101019 Stochastik
101024 Wahrscheinlichkeitstheorie
101026 Zeitreihenanalyse
101027 Dynamische Systeme
101028 Mathematische Modellierung
101029 Mathematische Statistik
101031 Approximationstheorie
102 Informatik
102001 Artificial Intelligence
102003 Bildverarbeitung
102004 Bioinformatik
102013 Human-Computer Interaction
102018 Künstliche Neuronale Netze
102019 Machine Learning
102032 Computational Intelligence
102033 Data Mining
305901 Computerunterstützte Diagnose und Therapie
305905 Medizinische Informatik
202035 Robotik
202037 Signalverarbeitung
103029 Statistische Physik
106005 Bioinformatik
106007 Biostatistik

JKU-Schwerpunkte

Digital Transformation

Andere Dateien und Links

https://arxiv.org/abs/2303.12783

Dieses zitieren

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title = "Conformal Prediction for Time Series with Modern Hopfield Networks",

abstract = "To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains. However, they are difficult to apply to time series as the autocorrelative structure of time series violates basic assumptions required by conformal prediction. We propose HopCPT, a novel conformal prediction approach for time series that not only copes with temporal structures but leverages them. We show that our approach is theoretically well justified for time series where temporal dependencies are present. In experiments, we demonstrate that our new approach outperforms state-of-the-art conformal prediction methods on multiple real-world time series datasets from four different domains.",

author = "Andreas Auer and Martin Gauch and Daniel Klotz and Sepp Hochreiter",

year = "2023",

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TY - GEN

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AU - Auer, Andreas

AU - Gauch, Martin

AU - Klotz, Daniel

AU - Hochreiter, Sepp

PY - 2023

Y1 - 2023

N2 - To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains. However, they are difficult to apply to time series as the autocorrelative structure of time series violates basic assumptions required by conformal prediction. We propose HopCPT, a novel conformal prediction approach for time series that not only copes with temporal structures but leverages them. We show that our approach is theoretically well justified for time series where temporal dependencies are present. In experiments, we demonstrate that our new approach outperforms state-of-the-art conformal prediction methods on multiple real-world time series datasets from four different domains.

AB - To quantify uncertainty, conformal prediction methods are gaining continuously more interest and have already been successfully applied to various domains. However, they are difficult to apply to time series as the autocorrelative structure of time series violates basic assumptions required by conformal prediction. We propose HopCPT, a novel conformal prediction approach for time series that not only copes with temporal structures but leverages them. We show that our approach is theoretically well justified for time series where temporal dependencies are present. In experiments, we demonstrate that our new approach outperforms state-of-the-art conformal prediction methods on multiple real-world time series datasets from four different domains.

UR - https://arxiv.org/abs/2303.12783

M3 - Conference proceedings

BT - Conference Neural Information Processing Systems Foundation (NeurIPS 2023)

ER -