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Parsimonious modeling with information filtering networks

Barfuss, W; Massara, GP; Di Matteo, T; Aste, T; (2016) Parsimonious modeling with information filtering networks. Physical Review E , 94 (6) , Article 062306. 10.1103/PhysRevE.94.062306. Green open access

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We introduce a methodology to construct parsimonious probabilistic models. This method makes use of information filtering networks to produce a robust estimate of the global sparse inverse covariance from a simple sum of local inverse covariances computed on small subparts of the network. Being based on local and low-dimensional inversions, this method is computationally very efficient and statistically robust, even for the estimation of inverse covariance of high-dimensional, noisy, and short time series. Applied to financial data our method results are computationally more efficient than state-of-the-art methodologies such as Glasso producing, in a fraction of the computation time, models that can have equivalent or better performances but with a sparser inference structure. We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors. The local nature of this approach allows us to perform computations in parallel and provides a tool for dynamical adaptation by partial updating when the properties of some variables change without the need of recomputing the whole model. This makes this approach particularly suitable to handle big data sets with large numbers of variables. Examples of practical application for forecasting, stress testing, and risk allocation in financial systems are also provided.

Type: Article
Title: Parsimonious modeling with information filtering networks
Open access status: An open access version is available from UCL Discovery
DOI: 10.1103/PhysRevE.94.062306
Publisher version: https://doi.org/10.1103/PhysRevE.94.062306
Language: English
Additional information: This is the published version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Physical Sciences, Physics, Fluids & Plasmas, Physics, Mathematical, Physics, Inverse Covariance Estimation, Statistical-Mechanics, Variable Selection, Financial-Markets, Lasso
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1572222
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