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

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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.

Original language	English
Title of host publication	Conference Neural Information Processing Systems Foundation (NeurIPS 2023)
Number of pages	48
Publication status	Published - 2023

Fields of science

305907 Medical statistics
202017 Embedded systems
202036 Sensor systems
101004 Biomathematics
101014 Numerical mathematics
101015 Operations research
101016 Optimisation
101017 Game theory
101018 Statistics
101019 Stochastics
101024 Probability theory
101026 Time series analysis
101027 Dynamical systems
101028 Mathematical modelling
101029 Mathematical statistics
101031 Approximation theory
102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102004 Bioinformatics
102013 Human-computer interaction
102018 Artificial neural networks
102019 Machine learning
102032 Computational intelligence
102033 Data mining
305901 Computer-aided diagnosis and therapy
305905 Medical informatics
202035 Robotics
202037 Signal processing
103029 Statistical physics
106005 Bioinformatics
106007 Biostatistics

JKU Focus areas

Digital Transformation

Cite this

@inproceedings{b4e3cabd239e40fe9e08167fcab62258,

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",

language = "English",

booktitle = "Conference Neural Information Processing Systems Foundation (NeurIPS 2023)",

}

TY - GEN

T1 - Conformal Prediction for Time Series with Modern Hopfield Networks

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 -

Conformal Prediction for Time Series with Modern Hopfield Networks

Abstract

Fields of science

JKU Focus areas

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