Normalizing Kalman Filters for Multivariate Time Series Analysis
Abstract
This paper tackles the modelling of large, complex and multivariate time series panels in a probabilistic setting. To this extent, we present a novel approach reconciling classical state space models with deep learning methods. By augmenting state space models with normalizing flows, we mitigate imprecisions stemming from idealized assumptions in state space models. The resulting model is highly flexible while still retaining many of the attractive properties of state space models, e.g., uncertainty and observation errors are properly accounted for, inference is tractable, sampling is efficient, good generalization performance is observed, even in low data regimes. We demonstrate competitiveness against state-of-the-art deep learning methods on the tasks of forecasting real world data and handling varying levels of missing data.
Cite
Text
de Bézenac et al. "Normalizing Kalman Filters for Multivariate Time Series Analysis." Neural Information Processing Systems, 2020.Markdown
[de Bézenac et al. "Normalizing Kalman Filters for Multivariate Time Series Analysis." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/debezenac2020neurips-normalizing/)BibTeX
@inproceedings{debezenac2020neurips-normalizing,
title = {{Normalizing Kalman Filters for Multivariate Time Series Analysis}},
author = {de Bézenac, Emmanuel and Rangapuram, Syama Sundar and Benidis, Konstantinos and Bohlke-Schneider, Michael and Kurle, Richard and Stella, Lorenzo and Hasson, Hilaf and Gallinari, Patrick and Januschowski, Tim},
booktitle = {Neural Information Processing Systems},
year = {2020},
url = {https://mlanthology.org/neurips/2020/debezenac2020neurips-normalizing/}
}