Dynamic time-lag regression: Predicting what & when
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.
|International Conference on Learning Representations (ICLR)
Chandorkar, M., Camporeale, E., Furtlehner, C., Poduval, B., & Sebag, M. (2020). Dynamic time-lag regression: Predicting what & when. In Eighth International Conference on Learning Representations.