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Dynamic Time Lag Regression: Predicting What and When

Abstract : This paper tackles a new regression problem, called Dynamic Time-Lag Regression (DTLR), where a cause signal drives an effect signal with an unknown time delay. The motivating application, pertaining to space weather modelling, aims to predict the near-Earth solar wind speed based on estimates of the Sun's coronal magnetic field. DTLR differs from mainstream regression and from sequence-to-sequence learning in two respects: firstly, no ground truth (e.g., pairs of associated sub-sequences) is available; secondly, the cause signal contains much information irrelevant to the effect signal (the solar magnetic field governs the solar wind propagation in the heliosphere, of which the Earth's magnetosphere is but a minuscule region). A Bayesian approach is presented to tackle the specifics of the DTLR problem, with theoretical justifications based on linear stability analysis. A proof of concept on synthetic problems is presented. Finally, the empirical results on the solar wind modelling task improve on the state of the art in solar wind forecasting.
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Submitted on : Friday, December 20, 2019 - 6:51:56 PM
Last modification on : Friday, November 18, 2022 - 9:23:18 AM
Long-term archiving on: : Saturday, March 21, 2020 - 8:59:39 PM


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  • HAL Id : hal-02422148, version 1


Mandar Chandorkar, Cyril Furtlehner, Bala Poduval, Enrico Camporeale, Michèle Sebag. Dynamic Time Lag Regression: Predicting What and When. ICLR 2020 - 8th International Conference on Learning Representations, Apr 2020, Addis Abeba, Ethiopia. ⟨hal-02422148⟩



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